text:
Fig. 1. (a) Spectral radiance of the human iris in the VW highly varies according to the levels of iris pigmentation, (b) which contributes for some of the noise factors (A, B, C) that degrade the quality of VW iris data. The bottom right corner plots the corresponding noise-free iris segmentation mask, obtained as described by He et al. [18]. (a) Iris spectral radiance [14]; (b) VW iris image [32].
UE TO the effectiveness proven by the deployed iris recognition systems, the popularity of the iris biometric trait has considerably grown in the last few years. As an illustration, over 50% of the papers cited in a recent survey [5] were published since 2005. Several reasons justify this interest: 1) it is a naturally protected internal organ that is visible from the exterior; 2) it has near circular and planar shape that more easily turns its segmentation and parameterization; and 3) its texture has a predominantly randotypic chaotic appearance that is stable over a lifetime. Regardless of a few recent innovations (e.g., the iris-on-themove project [21]), deployed iris recognition systems are quite constrained: subjects may stop-and-stare close to the acquisition device while their eyes are illuminated by a near-infrared (NIR) light source that enables the acquisition of good quality
Manuscript received March 05, 2010; revised September 13, 2010; accepted October 01, 2010. Date of publication October 11, 2010; date of current version February 16, 2011. This work was supported by FCT-Fundao para a Cincia e Tecnologia and FEDER in the scope of the PTDC/EIA-EIA/103945/2008 (NECOVID: Negative Covert Biometric Identication) research project. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Arun Ross. The author is with the Department of Computer Science, ITInstituto de Telecomunicaes, SOCIASoft Computing and Image Analysis Group, University of Beira Interior, 6200-Covilh, Portugal. Color versions of one or more of the gures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identier 10.1109/TIFS.2010.2086446
images. Recently, several research initiatives sought to increase distance, relax acquisition constraints, and make use of visible wavelength (VW) light imagery, which broadens the applicability of this technology to forensic domains where the subjects cooperation is not expectable. When compared to the traditional NIR constrained setup, the use of VW light and the uncontrolled lighting conditions engender notorious differences in the appearance of the captured data (Fig. 1). Acquiring images from signicantly larger distances and on moving targets demands simultaneously high -numbers and short exposure times for the optical system, in order to obtain acceptable depth-of-eld values. These are in direct proportion to the amount of light required for proper imaging. The American and European standards councils ([2] and [7]) proposed safe irradiance limits for NIR illumination of near 10 mW/cm . In addition to other factors that determine imaging safety (blue light, nonreciprocity, and wavelength dependence), these limits are a concern, as an excessively strong illumination can cause permanent eye damage. Moreover, the NIR wavelength is particularly hazardous, because the eye does not instinctively respond with its natural mechanisms (aversion, blinking, and pupil contraction). The pigmentation of the human iris consists mainly of two molecules: brown-black Eumelanin (over 90%) and yellow-reddish Pheomelanin [29]. Eumelanin has most of its radiative uorescence under the VW, which enables us to capture much more detail, but also more noisy artifacts [Fig. 1(b)]. The feasibility of the VW unconstrained iris recognition remains controversial, especially for high pigmented irises that constitute the majority of the worlds population, although preliminary experiments conrm the possibility of performing recognition in such challenging conditions [34].
As in many applications that deal with real-world data, segmentation is a major issue and has been motiving growing research efforts [35]: He et al. [18] used a clustering-based technique to roughly perform iris localization and an integro-differential constellation used for ne pupillary and scleric border detection, which not only accelerates the integro-differential operator but also enhances its global convergence. Proena [33] considered the sclera the most easily distinguishable part of the eye and proposed a classication procedure based in the neural pattern recognition paradigm that runs in deterministic linear time, making the procedure suitable for real-time applications. As illustrated in Fig. 1(b), these methods obtain acceptable results and effectively discriminate between the unoccluded regions of the iris (noise-free) and all the remaining data. Subsequent to segmentation, quality assessment is a fundamental task. The goal is to quantify characteristics and delity of the segmented data, particularly in terms of its utility. This is essential, as performing recognition in too much degraded data decreases matching accuracy and increases computational complexity. This paper describes a method to assess the quality of VW iris images acquired in uncontrolled lighting conditions, at large (4 to 8 m) distances and on moving subjects. When compared with previously published approaches, several discriminating factors can be pointed out: 1) The type of data that we aim to deal with differs. To the best of our knowledge, all the previously published methods were devised to NIR data acquired in constrained setups,
while our proposal handles the specicities of real-world VW data. 2) We empirically observed that published methods tend to fail in assessing the quality of VW data. 3) We use the output of the segmentation stage in all quality assessments, which improves performance in some assessments (such as focus, described in Section III-C) and makes others easier to assess (such as occlusions, described in Section III-F). The remainder of this paper is organized as follows: Section II summarizes the most recent proposals to assess iris image quality, grouping them according to various criteria. In Section III, we describe the proposed method in detail. Section IV reports the experiments and discusses our results. Finally, Section V concludes and points out further work. II. RELATED WORK The concept of a good metric is not trivial to determine, although the best one should maximally correlate with recognition effectiveness. Previous studies reported signicant decays in effectiveness when data is degraded by each of the factors listed in Table I. Here, we overview the main techniques used to assess iris image quality with respect to each factor and compare them according to the used spectrum of light, the type of analyzed data (raw image, segmented or normalized iris region) and their output (local or global), as they operate at the pixel or image level. We note that most of the methods operate
Fig. 2. Cohesive perspective of the proposed quality assessment method. The iris segmentation mask is used to roughly estimate the center of the iris. This value is used as a reference point to reconstruct the deemed biological iris boundary and to assess each of the seven quality measures.
in NIR images and assess quality in the segmented data (either in the cartesian or polar coordinate systems). Exceptions are usually related with focus measurement, obtained by one of two approaches: 1) measuring the high frequency power in the 2-D Fourier spectrum through a high-pass convolution kernel or wavelet-based decomposition ([10], [20], and [6]); 2) analyzing the sharpness of the iris borders through the magnitude of the rst- and second-order derivatives ([1] and [43]). Another key characteristic is the level of analysis: some methods operate globally (at the image level), usually to determine focus, gaze, or motion blur ([20], [24], and [40]). As image quality varies across the iris, others operate at the pixel level to determine local obstructions ([1], [22], and [31]). Motion is estimated by detecting interlaced raster shear that might be due to signicant movements during the acquisition of a frame ([11], [28], [41], and [47]). Other approaches rely in the response of the convolution between the image and directional lters, being observed that linear motion blurred images have higher central peak responses than sharp ones ([22] and [25]). Gaze is estimated by 3-D projection techniques that maximize the response of the Daugmans integro-differential operator [22] and by the length of the axes of a bounding ellipse [47]. Eyelids are detected by means of line and parabolic Hough transforms [17], active contours [26], and machine learning frameworks [31], [42]. The modal analysis of the intensities histogram enables the detection of eyelashes [10], [17], as do spectral analysis [28] and edge-based methods [22]. As they usually are the brightest regions of images, specular reections are detected by thresholds [22], while diffuse reections are exclusive of VW data and more difcult to discriminate, being reported a method based in texture descriptors and machine learning techniques [31].
is roughly found and used [18]. The center of the iris to parameterize the deemed biologic pupillary and limbic iris and ). These boundaries permit boundaries ( assessment of the quality of severely degraded samples that will be extremely difcult to handle by traditional approaches. For comprehensibility, the generated quality scores are denoted for focus, for everywhere by and a subscript: motion blur, for off-angle, for occlusions, for iris for pixel count (area), and for pupillary pigmentation, dilation. As it is given in Table I, these quality factors are known to be strong correlated with recognition effectiveness and its assessment is made in a specic way for iris data. Other factors (such as Gaussian or salt and pepper noise) that are assessed in iris data as in any other type of images were not the scope of this work. A. Estimation of the Iris Center The convergence property of convex surfaces is the key insight this phase. The external boundary of the iris is predominantly convex, which was used to trace rays that pass through it and bend perpendicularly, converging into a focal point that gives an estimate of the iris center. As shown in Fig. 3, this gives a rough estimate of the true center, even in cases where the amount of unoccluded iris is very small. This approximation is acceptable because it is used as a reference point by the shape descriptor that parameterizes the iris boundaries (normalized cumulative angular), which is invariant to the reference point as long as it is inside the desired contour. columns) and the Let be an iris image ( rows binary mask that gives the nonparameterized segmentation of its noise-free iris regions (bottom right corner of Fig. 1). Let be the set of edge pixels of , each with coordinates . Let be a vector tangent to the iris its perpendicular. A line through boundary at and point and perpendicular to is given by (1)
III. PROPOSED METHOD A global perspective of the proposed method is given in Fig. 2. The input is a VW iris sample and its corresponding segmentation mask, obtained according to the method of
. Let and with coordinates , be the noise-free iris pixels at, respectively, the farthest and in regularly spaced direcclosest distances from tions
(3) and give the outer and inner iris boundaries, and the former is illustrated in Fig. 4(a). Special attention should be paid to extremely occluded irises, where some of the might not exist and should be discarded of further processing by a simple semantic rule. Next, the cumulative angular function [45] is used as shape descriptor of each type of boundary, dened as the amount of angular change from an arbitrary starting point (4) and describes changes in direction at where point with respect to changes in arc length . Here, if the boundary is a perfect circle, the corresponding angular descripwill be . An illustration of the tion values is given in Fig. 4(b). Segments of the boundary that correspond to biological borders are discriminated by statistical estiand values. As mation of the illustrated in Fig. 4(b)(d), biological boundaries have evident smoother values and lower energy, which gives raise to different values of the objective function (5) give the coordinates of the iris center. Fig. 3 gives where two examples of this procedure: images in the left column have notoriously different proportion of noise-free iris and images values. in the right column give the accumulated In both cases, the estimated center of the iris (denoted by the interception of the dashed lines) is acceptably close to the actual center. B. Parameterization of the Biological Iris Boundaries Efcient parameterization of the iris boundaries that are behind occlusions is a key issue regarding iris image quality assessment. Such boundaries not only permit us to infer gaze, but also to estimate the proportion of occluded iris. This phase can be divided into two steps: 1) discriminate between the boundaries that correspond to iris biological borders and those that delimitate noisy regions; 2) reconstruct the biological iris boundary, according to the former boundary segments. The key insight is that biological boundaries can be faithfully described by periodic signals, which justies the use of Fourier series be the center of the iris and for such purpose. Let the Euclidean distance between pixels in a given direction , where and the noise-free iris are given by where are regularization constants empirically obtained. Arregularly spaced in guments of the rst quartile of are deemed to belong to the biological border and their coordinates (column and row) are illustrated by the dot and cross data points of Fig. 4(e). The reconstruction of the complete biological border starts from these coordinates and is regarded as a nonlinear regression of a Fourier series of order , given by (6) Constraining the fundamental frequency to assures closure and completeness of the obtained contour. For each boundary, both the column and row coordinates are tted simidata points , the goal is to optimize larly. Given a set of so as to minimize the parameter vector the sum of squares of the deviations (7) where is the desired value at and the actual response. Requiring the zero-gradient condition to hold at the minimum (8)
Fig. 3. Estimated iris center (interception of the dashed lines) in a relatively unoccluded iris (upper row) and a heavily occluded one (bottom row). Images of the right column give the accumulated (x; y; l ) values.
with . Let be the set of lines traced from . We dene an indicator function that veries wether the pixel belongs to
Fig. 4. Estimation of the biological scleric border. The boundary points that are farthest from the iris center at each direction (a) are used to obtain the cumulative angular description
(t ) of the contour (b) and its rst- and second-order derivatives (c) and (d). The selection of the regions deemed to belong to the iris biological boundary [cross and circular data points of (e)] enables the reconstruction of the deemed biological border through a regression of a Fourier series (f). (a) b , (b)
(t ), (c) (@
(t))=(@t) 3 G(; r ), (d) (@
(t))=(@t ) 3 G(; r ), (e) Fourier regression, (f) reconstructed boundary.
is the Jacobian. Let , the zero gradient is given by , which is solved iteratively using the NewtonRaphson iterations and starting from an initial guess of , corrected to . The increment satises the linear equation , is the Jacobian of where (9) As satises the system of linear equations (10) (11) (12)
image by the 2-D Fourier transform of a Gaussian kernel [9]. Thus, focus of an image can be assessed by measuring its amount of high frequencies, i.e., the accumulated power of the convolution between and a high-pass kernel (13) This measure performs well if images are dominated by the iris. Otherwise, if the iris is just a small part of the datadominated by eyelashes or hair that are full of minutiait tends to fail, which is particularly probable in uncontrolled image acquisition setups. Hence, focus is exclusively assessed in the region segmented as noise-free iris (using the segmentation mask ), avoiding the described problem. Also, assuming that segmentation inaccuracies predominate near the boundaries, we compared the results obtained when using exclusively the most interior regions of the noise-free irises (obtained by morphologic and illustrated in the top images of Fig. 5), that erosion of be have higher probability of actually being noise-free. Let an iris segmentation mask eroded by a circular structuring element of radius . In order to keep the proportion of iris removed by erosion independent of the iris size, the radius of the structuring element is given by (14) is an empirically adjusted threshold. In the experwhere iments, we used a data set of 1000 focused images of the and convolved them with Gaussian UBIRIS.v2 database
Assuming moderate nonlinearity, a reasonable approximation of is given by , obtaining the next generation of the parameter vector . This is repeated until convergence is reached, i.e., , where is a very small positive value. An illustration of the reconstructed biological iris boundary is given in Fig. 4(f). C. Focus Assessment As Table I summarizes, the Fourier domain is the most frequently used in focus assessment. The rationale is that focused data contains more high-frequency components than blurred data. Daugman pointed out that defocus is equivalent to multiplying the 2-D Fourier transform of a perfectly focused
Fig. 5. The upper row shows an iris mask and its increasingly most interior iris regions. The bottom left gure shows the separation between the focus scores obtained in defocused versions of the training data set, with respect to different values of T . The bottom center and right plots illustrate the improvement observed when just the most interior iris regions are used as source data (dotted lines), instead of the whole image (dashed lines) or whole iris (continuous lines).
kernels , obtaining three . increasingly defocused versions of the data set: As it is given in the bottom row of Fig. 5, we compared the values obtained by the Daugman [9] and Kang and Park [23] kernels when using: 1) the whole image, 2) the whole iris, and 3) the most interior iris regions as input data. The plot at the bottom left corner gives the proportion between the average values of consecutive data sets, i.e., , where is the average focus value in the data set. (Daugman Maximal separation was obtained around (Kang and Park kernel), which is justikernel) and ed by the smaller size of the latter kernel. Plots at the center and right contextualize the results, comparing the values obtained when using the whole image (dashed lines), the whole iris , (continuous lines), and the interior iris regions (with dotted lines) as source data. It can be conrmed that both spectral measures perform acceptably in VW data and that results were consistently improved when exclusively the most interior obiris regions were used as input (defocused versions of tained the lowest focus scores). D. Motion Assessment There are various causes for motion blurred iris images, corresponding to the different types of movements in the scene: subjects, heads, eyes, and eyelids, causing motion to be nonlinear and particularly hard to determine and compensate for. In previous works, an oversimplication was made and assumed that motion is linear across the image, which cannot be guaranteed in unconstrained acquisition setups. Instead, we exclusively assumed that motion is linear in the iris ring and we are concerned about the detection of linear motion blur inside . The rationale is that linan iris bounding rectangle early motion-blurred data has frequency responses with visible parallel strips of direction that correspond to the motion direction. As suggested by Kalka et al. [22], the width
Fig. 6. Power cepstrum of a sharp iris (a) and of two motion blurred irises [(b) and (c)]. Figure (d) gives the accumulated image derivatives along directions, where the minimum values indicate the primary blur direction. The amplitude of this signal is also used to discriminate between motion blurred (1). (a) No motion. (b) Blurred, and sharp irises, as (2); (3) (; ) = (( )=(2); 0:22). (c) Blurred, (; ) = (( )=(4); 0:21). (d) Directional derivatives.
and power of these strips provide an estimate of the amount of motion. We used the concept of power cepstrum , illustrated in three images of Fig. 6: a sharp and two motion-blurred. The primary direction of motion is deemed to be the one that minimizes the power of the derivaalong a direction tives of (15) gives the partial directional derivative of direction . The amplitude of the
along the
Fig. 7. Examples of the proposed off-angle assessment, for aligned, moderately, and severely deviated iris images.
E. Off-Angle Assessment Assuming the circularity of the pupil as a measure of the off-angle, Kalka et al. [22] and Dorairaj et al. [13] projected an integro-differential operator at multiple yaw and pitch angles, yielding an optimization process in a 5-D hyperspace with signicant computational efforts. Daugman [10] used Fourier series expansions of the 2-D coordinates of the pupillary boundary, which contain shape distortion information that is related to deviated gaze. Expressing the pupillary boundary and , the direction as a set of coordinate vectors and magnitude of the gaze deviation are contained in the form and of Fourier coefcients on the harmonic functions thatlinearly combinedrepresent and . Algebraic manipulation estimates from four Fourier coefcients two gaze deviation parameters and . Our aim is to obtain a unique value that gives the magnitude of the deviation (used as quality score) and another one that corresponds to its major direction. The rationale is that the degree of circularity of the scleric boundary should be inversely correlated to off-angle acquisition. For such, we used the concept of minimal bounding rectangle and compared the length of its sides. Let be the coordinates of the biological scleric boundary, that delimitates a convex polygon . As proposed in [16], are the coordinates of the four and stand for the minimum extreme points of , where , be four line segments and maximum values. Let through the extreme points and the minimal rotation such that one coincides with a line segment dened by . Let be the versions of rotated by . Minimizing the area of the enclosing rectangle is equivalent to keep the minimum of successive rotations, until the lines have been be the rotated by an angle greater than 90 . Let line segments that delimitate the minimum enclosing rectangle . Let and be of extreme points . The the lengths of these line segments, such that
where , such that and . Fig. 7 illustrates three iris images and the corresponding off-angle scores: image in the left column was frontally captured and the others resulted of moderated (central column) and severe deviations (right column) in image acquisition. F. Occlusions Assessment An estimate of the amount of iris that is occluded by other types of data is obtained by comparing the area inside the deemed biologic iris ring (inside the scleric and ), and the area of outside the pupillary contour the noise-free iris segmentation mask. Assuming the convexity ,a of the polygon dened by the set of vertices pixel is on the interior of this shape if it is always on the same . side of all line segments dened by , be a function that Let relates the position of to the straight line dened by . An indicator function that discriminates between pixels inside and outside the iris ring is given by
(18) and . Function has direct correspondence to the proportion of iris occluded by noise where
(19) where is the area of the iris ring. Fig. 8 gives some examples of this procedure: white pixels denote regions segmented as noisefree that are inside the deemed biological boundaries; dark and light gray regions, respectively, denote occluded iris pixels and regions outside the deemed biological boundaries. It can be conrmed that assessments match the intuitive human perception,
Fig. 8. Examples of the proposed iris occlusions assessment, for practically unoccluded, moderately, and severely occluded iris images. White regions were segmented as noise-free and are inside the deemed biological boundaries. Dark and light gray regions, respectively, denote noisy iris pixels and regions outside the biological boundaries.
either for almost unoccluded, moderately, or severely occluded iris images. G. Levels of Iris Pigmentation The spectral radiance of the iris with respect to its level of pigmentation has greater variance in the VW than in the NIR, which increases the heterogeneity of the captured data: light pigmented irises provide more detail than heavy pigmented, although the number of visible crypts, arching ligaments, freckles, and contraction furrows signicantly varies between subjects. Also, the appearance of the striated anterior layer that covers the trabecular meshwork depends of the amount of light used in the acquisition process. Here, we propose a strategy to assess the levels of iris pigmentation based in the observation that local hue and value across the iris vary accordingly to its levels of pigmentation. Also, we consider the average brightness of a region deemed to contain the sclera, adjusting for different be the iris image represented in the lighting conditions. Let be the set of edge pixels of HSV color space. Let that are farthest from in regularly spaced directions . Let be the Euclidean distances between elements of . The third quartile of corresponds to the farthest distances between edges, which associated vectors tend to point to the direction of the sclera (avoiding eyelids and eyelashes occlusions). The direction of the sclera is given by the mean direction of the vectors drawn from . these farthest points, i.e., delimitate an image strip that spreads in Thus, and is illustrated in the left column of Fig. 9. the direction inside the band Let be a binary mask, such that and otherwise. The levels of iris pigmentation are obtained by relating the hue and value of the iris and of the sclera pixels inside this band
Fig. 9. Images at the right column illustrate the image band deemed to contain the sclera. Plot at the center gives the values of a training set of 300 images, where circular and cross points represent light and heavy pigmented irises.
and is a regularization term. According to a set of manually classied 300 iris images of light and heavy pigmented irises, a nearest neighbors strategy classies the , where sample into one of classes and represent the number of light and heavy pigmented irises in the nearest neighbors. The plot at the right of Fig. 9 values of the training set, where circular and cross gives the data points represent light and heavily pigmented irises. H. Pixel Count, Pupillary Dilation, and Amount of Information Three other factors that are known to play relevant roles in the recognition effectiveness are discussed in this section: the area of the noise-free data, the pupillary dilation, and the amount of information within the iris, which were grouped in a single section due to the simple way they are obtained. The area of the nonoccluded iris region is simply given by the number of pixels classied as noise-free by the segmentation algorithm, i.e., . An assessment of the pupillary dilation is given by the ratio between the area of the polygons dened by the scleric and pupillary biological boundaries, both estimated in Section III-B. Finally, an estimate of the amount of information can be given by the local entropy values in small windows of the iris ring. However, we decided not to use it as quality score, since we observed that this value is heavily correlated with the focus score and relatively invariant to other parameters (such as acquisition distance and levels of iris pigmentation).
Fig. 10. Quality assessments obtained for the UBIRIS.v2 database. For all plots, better quality are expressed by rightmost bars. For the iris pigmentation plot, light pigmented irises are represented by rightmost bars. (a) Focus. (b) Motion. (c) Off-angle. (d) Occlusions. (e) Iris pigmentation. (f) Iris area. (g) Pupillary dilation.
I. Temporal Computational Complexity The computational complexity of the given method was a major issue, in order to handle real-time data. Here we analyze the complexity of each phase and give statistics about its execution time. It should be noted that values should be regarded as upper bounds, as they were obtained using an interpreted language and without any performance optimization concerns. pixels and be its Let be an RGB image with corresponding binary segmentation mask (in our experiments ). Although this is a relatively large value, the phases with highest computational complexity depend of the of in directions ( , benumber of edge pixels tween 64 and 512 in our experiments). As these are relatively low values, the increased computational complexity is not a concern and does not signicantly affect performance. The estimation of the center of the iris (an evidence-gathering . The parameterizaprocess) has a time complexity of tion of the iris boundaries receives values, described by and used in means of the cumulative angular descriptor a nonlinear regression of a Fourier series with time complexity . Focus is assessed by the convolubounded by tion between the noise-free iris pixels and a high pass kernel, which can be done in quadratic time with respect to . Motion was assessed by means of the power cep, strum of the pixels within an iris bounding box having time complexity . Off-angle assessment uses the concept of minimal bounding rectangle and of its minor and major axis, which runs in time deterministic linear to its input . Occlusions are assessed by measuring the proportion between two areas: one given by a point-in-polygon algorithm iris pixels (linear complexity) and the other composed by the . Finally, the levels of iris pigmentation are estimated by the average hue and value in an image band, with respect to a ). In our earliest experdata training set, which is done in iments we used a lazy classication algorithm (nearest neighbors), and then explicitly dened the decision boundaries, which has signicantly improved performance.
Experiments were performed in a 2.66-GHz iMac with 8-GB 1067-MHz DDR3 memory, using an interpreted programming language and without any performance optimization concerns. Even so, the observed average execution time for the complete for , for assessment was , and for images (95% condence intervals). These values led us to conclude that, if appropriate programming languages and hardware are used, performance should easily be improved by two orders of magnitude and real-time performance achieved. IV. EXPERIMENTS This section describes our experiments during the evaluation of the proposed method. Several examples are illustrated and discussed, the used data sets described, and the improvements in recognition effectiveness highlighted. Finally, we discuss the adaptability of the proposed method to NIR data. A. Results and Discussion For the purpose of contextualization, our method was evaluated in two dramatically different VW iris data sets: UBIRIS [32] and UPOL [39]. The former ts better the scope of this work and was used as the main experimental data source. Its images were captured from moving subjects, at varying distances (4 to 8 m) and in uncontrolled lighting conditions, resulting in extremely heterogeneous images with several factors that degrade their quality. Oppositely, UPOL images were acquired with an optometric framework and according to a rigid protocol, which resulted in almost noise-free images. In order to avoid that segmentation errors corrupt the results, a subset of 10 427 images was selected, which under visual inspection we veried that the method of He et al. [18] has accurately segmented. We performed quality assessment in this set, obtaining the histograms given in Fig. 10. In order to better match the human perception of quality, scores were grouped according to their numeric range. Each group is shown as one bin labeled in the [1, 10] discrete range, regarded as a quantized
Fig. 11. Examples of the proposed quality assessments. Seven quality scores are given (focus, motion, off-angle, occlusions, pigmentation, pixel count, and pupillary dilation), along with the corresponding quantized and normalized scores between parenthesis. For the focus, motion, off/angle, occlusions, iris area, and pupillary dilation scores better values correspond to higher scores. For the pigmentation scores, higher values correspond to the lightest pigmented irises.
and normalized quality score. For comprehensibility, normalized scores are denoted by plus the same subscripts as before. The horizontal range of the leftmost and rightmost bins extends to include the entire data range. For focus, motion, off-angle, occlusions, iris area, and pupillary dilation plots, better quality values are given in the rightmost bars (in the pigmentation plot rightmost bars denote the lightest pigmented irises). It can be seen that the large majority of images were classied as focused and without signicant motion-blur. We observed that over (1/3) of them are off-angle and about (1/2) have moderated to severe iris occlusions. Most of the subjects (Caucasian Latin) have dark and medium dark irises that were captured from varying distances, which resulted in irises of very different sizes. These results appear to be in agreement to the acquisition protocol of the data set [32], which we consider a good metric for the effectiveness of the proposed quality assessment.
Fig. 11 gives several examples of our quality assessments, showing the original quality score and the corresponding between parenthesis (acquantized and normalized value cording to the previously found horizontal range values).1 The is an example given in the rst column and left row image of relative good quality, from where 128 points deemed to belong to the biological boundary were extracted. These came mostly from the inferior parts of the iris due to minor score occlusions in its upper region, which justies the good (9 in 10). The image was classied as focused and the iris as light pigmented, which matches the human perception. Examand illustrate two severely ples given in positions degraded samples, from where the biological borders were correctly estimated. In both cases, the selected points came1More examples can be found at http://www.di.ubi.pt/~hugomcp/doc/examplesVWIQ.htm.
from the nasal and temporal boundaries of the iris, which are known to have smaller probabilities of being occluded. From our viewpoint, these are good examples of the effectiveness of the proposed strategy in dealing with severely degraded data. is from the UPOL database and is Image at position noise-free, which has evident correspondence in the almost maximal normalized quality scores. Finally, gure in illustrates a failure case due to problems in discriminating between the biological and noisy boundaries. This occurs mostly when the noisy boundaries are very smooth and deemed as biological by the objective function (5). We hope that a set of semantic rules avoids this problem. B. Improvements in Recognition Effectiveness In evaluating the improvements in recognition effectiveness achieved due to quality assessment, we used the classical Daugmans recognition strategy [10], as it is unique in that it is actually functioning in commercially deployed systems and is clearly the most well known. According to this choice, the segmented boundaries were normalized to dimensionless polar coordinates. Then, a bank of Gabor lters was used to analyze the iris texture and the angle of each phasor quantized to one of four quadrants. Finally, the fractional Hamming distance gave the dissimilarity between two irises. As suggested by Daugman [8], for two-choice decisions (e.g., match/nonmatch), the decidability index measures how well separated the two types of distributions are and recognition errors correspond to their overlap area. It is given by (21) where and are the means of the two distributions and and their standard deviations. Our tests were performed in an one-against-all comparison scheme. Fig. 12 compares the histograms of the fractional Hamming distances obtained for the match (light bars) and nonmatch (dark bars) comparisons without quality assessment [Fig. 12(a)] and when the quality scores are used to reject samples with below 5 [Fig. 12(b)] and 7 [Fig. 12(c)]. The line plots correspond to the tted Normal distributions and the upper left corner gives the corresponding decidability index . As general considerations, we observed that values obtained for the nonmatch distributions were similar to values reported for the NIR setup, which points that the probability for a false acceptance is independent of data quality. Oppositely, there is a signicant movement of the match distributions toward the nonmatches, substantially increasing the probability for false rejections if traditional acceptance thresholds are used. Due to this, the decidability of the VW recognition systems is much lower than the corresponding value reported for the NIR setup. Interestingly, the observed primary effect of quality assessment was to counterbalance the above referred movement, as illustrated in Fig. 12(b) and (c). It can be seen that match distributions tend to become narrower and slightly to the right when samples with quality and were not used in the recognition tests. This is reiterated in Fig. 12(d) which compares the match (dashed lines) and nonmatch (continuos lines) distributions according to the minimum value of . We concluded that
Fig. 12. Comparison between the distributions of the fractional Hamming distances for match (white bars) and nonmatch (gray bars) distributions, according to the minimum quality of samples q . (a) Fractional Hamming distances without data quality assessment. (b) Fractional Hamming distances between samples with minimal quantized quality above 5 (q 5). (c) Fractional Hamming distances between samples with minimal quantized quality q above 7 (q 7). (d) Fractional Hamming distances with respect to the minimal quality of samples (q ).
Fig. 13. Improvements in recognition performance due to quality assessment. (a) ROC curves corresponding to the distributions illustrated in Fig. 12. (b) Variation in the PR decidability index d according to the minimal image quality.
quality assessment improves recognition effectiveness by consistently shifting the histogram of the match comparisons to the left, which improves the decidability of the resulting PR systems and is of specially interest for noisy acquisition environments, where both types of distributions largely overlap. Moreover, the amplitude of such match movement appears to be in direct proportion to the minimal quality of samples. Fig. 13(a) shows how the true and false matches in our system would change according to different decision thresholds, when no quality is considered (continuous line) and when only sam(dasheddotted line) and (dashed line) ples with are considered for recognition. Here, we plot the area under curve (AUC) for all three setups, which signicantly augments as the poorest quality samples are rejected. Fig. 13(b) shows how the decidability values vary with respect to the minimum value of . The gray regions denote condence intervals of 95%. Again, we conrmed that samples with poor quality considerably shift the histogram of the match distributions toward the
Fig. 14. Degradation in recognition effectiveness due to each of the quality factors assessed in this paper. Line plots relate the decidability index d to the minimal quantized and normalized quality scores of the used samples. The gray regions denote 95% condence intervals. (a) Degradation in recognition effectiveness due to focus and motion blur. (b) Degradation in recognition effectiveness due to off-angle and pupillary dilations. (c) Degradation in recognition effectiveness due to the available area and iris occlusions. (d) Degradation in recognition effectiveness due to the levels of iris pigmentation.
nonmatches, and consistently reduce the separability between both distributions. We know of two possible explanations for the sigmoid appearance of the decidability curve: 1) for some of the noise factors (e.g., blur) it appears that just heavily degraded data degrades performance and 2) the excessive number of beans used in score quantization leads that data of adjacent normalized scores has actually very similar quality that, even for humans, is very difcult to discriminate. C. Analysis of Individual Factors The next question to consider is about the effect of each quality score in recognition effectiveness. For such, we repeated the one-against-all recognition setup, but selecting exclusively samples with minimal quality according to each type of score. Fig. 14 gives the obtained decidability values with respect to the quantized quality scores in each type as in the previous experiments). of assessment ( Again, rightmost regions correspond to data with better quality and the gray regions denote 95% condence intervals. It should be noted that although this experiment enables to perceive how much recognition effectiveness is degraded by each type of quality score, it cannot be regarded as an effective evaluation. This will demand a data set with images degraded exclusively by one factor and UBIRIS.v2 images (as well all the remaining available data sets) are simultaneously degraded by various factors. Even so, several degradation patterns are evident and able to be discussed. At rst, the major effect that gaze deviations have in recognition effectiveness [Fig. 14(b)], when compared to other factors. We suspect that such perspective deformations cause signicant changes in phase, which signicantly perturb
the used feature extraction and matching strategies. We also conrmed the minor impact of focus in recognition effectiveness [Fig. 14(a)], as it is known that most of the iris discriminating information spans between 2 and 3 octaves of the original scale and only severe defocus would notoriously affect the recognition process. In our opinion, this is one of the biggest advantages of the iris, when compared to other biometric traits: using the lowest and middle-low frequency components for recognition improves the robustness to noise (focus and artifacts). Our observations about the degradation in effectiveness due to pupillary dilations were inconclusive, in opposition with previously published works (e.g., [19]). We suspect that this was mainly due to the spectrum of light of our data sets: in opposition to NIR data, the pupillary boundary of VW images is much more difcult to segment than the scleric. For such, signicant inaccuracies in the segmentation of the pupil might have occurred and corrupted the pupillary dilation experiments. Fig. 14(d) conrms that the levels of iris pigmentation constitute an obstacle to VW iris recognition, as the decidability values obtained for light pigmented irises (rightmost data points, mostly blue and light green irises) were notoriously higher than those obtained when heavily pigmented images were also used. One probable reason for this is the spectral absorbance of the predominant pigment of the human iris (brown-black Eumelanin), that has a peak in the VW and turns the discriminating patterns of heavily pigmented irises (ridges, valleys, furrows, and crypts) more difcult to capture. The experiments about the occlusions and area factors [Fig. 14(c)] enabled us to conclude that effectiveness is directly correlated to the available area of the iris. This can be observed just up to a limit (around the sixth quality bean), above which effectiveness did not consistently improve. Also, the higher decidability observed for bigger irises may be related to the fact that images of UBIRIS.v2 that were acquired at close distances tend to have proportionally less proportion of occluded iris, due to the specic lighting conditions of the environment. Finally, we tested the independence between the generated quality scores. Table II gives the Pearsons sample correlation , obtained coefcient for each pair of quality scores for the UBIRIS.v2 data set
(22) gives the quality score of sample and its mean where value. Moderate to signicant correlation was observed between the (focus, motion), (off-angle, occlusions), (off-angle, area), (occlusions, pupillary dilation), and (area, pupillary dilations)
of such NIR data sets. Further, the improvements in performance observed when using exclusively the most interior parts of the iris in focus assessment were conrmed. Also, the phases described in Sections III-D and III-E, that perform with lower computational cost when comparing to the work described in [22], appear to produce plausible results in NIR data too. V. CONCLUSION Due to potential forensic applications, the possibility of performing iris recognition in uncontrolled lighting conditions and at larger distances motivates growing research efforts. Here, quality assessment is a key issue, due to the factors that typically degrade the quality of data acquired in such acquisition setup. Most of the published approaches to assess iris image quality were devised to deal with NIR controlled data and do not appropriately handle the the specicity of real-world VW data. This motivated the proposal of an automated method to assess the quality of such data, in terms of the factors that are known to be correlated with biometric effectiveness: focus, motion, angle, occlusions, area, pupillary dilation, and levels of iris pigmentation. The proposed quality assessment strategy starts by the estimation of the center of the iris and of its deemed biological boundaries. These are used to assess each quality factor and generate a numeric value thatempiricallywas observed to be in agreement with the human visual notion of quality. Also, we reported consistent improvements in performance due to data quality assessment, when the poorest quality samples were not considered for the recognition process. REFERENCES scores. With exception to the former, all the remaining have evident biological roots, which contributes to the successful empirical evaluation of the proposed iris quality assessments. D. Adaptability to NIR Images Even considering that the proposed method was devised to handle VW data, it might be useful to discuss its adaptability to NIR data that corresponds to what is currently used in commercially deployed systems. For such, we adopted a parameterized segmentation method composed of an elliptical integro-differential operator [38] that gives the coordinates of the limbic and pupillary boundaries, and then used active contours based on Fourier series (with 17 and 4 coefcients for pupillary and limbic boundaries) for ne representation [10]. Specular reections were detected by means of hard thresholds and eyelashes by modal analysis of the intensities histograms within the iris. Then, based in the NIR sample and its segmentation mask, all the subsequent phases of the proposed method perform similarly (as overviewed in Fig. 2), with the exception to the iris pigmentation assessment that becomes meaningless. Also, it should be emphasized that some of the quality assessment outputs have different ranges of those illustrated in Fig. 10 and new reference values have to be obtained. Fig. 15 illustrates two quality assessments for images of the CASIA.v3 (Lamp) and WVU iris databases. Not surprisingly, the observed pupillary dilation scores were signicantly lower than those typically obtained in VW data (pupils more dilated), which led us to suspect that there were reduced amounts of visible light in the acquisition scenes[1] A. Abhyankar and S. Schuckers, Iris quality assessment and bi-orthogonal wavelet base decoding for recognition, Pattern Recognit., vol. 42, pp. 18781894, 2009. [2] American national standard for the safe use of lasers and LEDs used in optical ber transmission systems, ANSI Z136.2, American National Standards Institute, 1988. [3] E. Arvacheh and H. Tizhoosh, A Study on Segmentation and Normalization for Iris Recognition, M.Sc. dissertation, University of Waterloo, Waterloo, ON, Canada, 2006. [4] A. Basit and M. Y. Javed, Iris localization via intensity gradient and recognition through bit planes, in Proc. Int. Conf. Machine Vision (ICMV 2007), Dec. 2007, pp. 2328. [5] K. Bowyer, K. Hollingsworth, and P. Flynn, Image understanding for iris biometrics: A survey, Comput. Vis. Image Understanding, vol. 110, no. 2, pp. 281307, 2008. [6] Y. Chen, S. C. Dass, and A. K. Jain, Localized iris image quality using 2-D wavelets, in Proc. Int. Conf. Biometrics, 2006, pp. 373381. [7] Photobiological Safety Standards for Safety Standards for Lamps Commission International de lEclarirage, Rep. TC 6-38; (CIE 134-3-99), 1999. [8] J. G. Daugman, Biometric Decision Landscapes Tech. Rep. TR482, 2000 [Online]. Available: http://www.cl.cam.ac.uk/users/jgd1000/ biomdecis.ps [9] J. G. Daugman, How iris recognition works, IEEE Trans. Circuits, Syst., Video Technol., vol. 14, no. 1, pp. 2130, Jan. 2004. [10] J. G. Daugman, New methods in iris recognition, IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 37, no. 5, pp. 11671175, Oct. 2007. [11] J. G. Daugman and C. Downing, Effect of severe image compression on iris recognition performance, IEEE Trans. Inf. Forensics Security, vol. 3, no. 1, pp. 5261, Mar. 2008. [12] M. Dobes, J. Martineka, D. S. Z. Dobes, and J. Pospisil, Human eye localization using the modied hough transform, Optik, vol. 117, pp. 468473, 2006. [13] V. Dorairaj, N. Schmid, and G. Fahmy, Performance evaluation of nonideal iris based recognition system implementing Global ICA encoding, in Proc. Int. Conf. Image Processing, 2005, vol. 3, pp. 285288.
Fig. 15. Examples of the application of the proposed iris quality assessments to NIR images of the CASIA.v3 Lamp (left column) and of the WVU (right column) databases.
[14] F. Imai, Preliminary Experiment for Spectral Reectance Estimation of Human Iris Using a Digital Camera Munsell Color Science Laboratories, Rochester Institute of Technology, Tech. Rep., 2000. [15] K. Grabowski, W. Sankowski, M. Zubert, and N. Napieralska, Focus assessment issues in iris image acquisition system, in Proc. 14th Int. Conf. MIXDES 2007, 2007, pp. 628631. [16] G. Toussaint, Solving geometric problems with the rotating calipers, in Proc. IEEE MELECON83, Athens, Greece, May 1983. [17] Z. He, T. Tan, Z. Sun, and X. Qiu, Robust eyelid, eyelash and shadow localization for iris recognition, in Proc. Int. Conf. Image Processing, 2009, pp. 265268. [18] Z. He, T. Tan, Z. Sun, and X. Qiu, Towards accurate and fast iris segmentation for iris biometrics, IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 9, pp. 16701684, Sep. 2009. [19] K. P. Hollingsworth, K. W. Bowyer, and P. J. Flynn, The importance of small pupils: A study of how pupil dilation affects iris biometrics, in Proc. Int. Conf. Biometrics, 2008, pp. 16. [20] J. Jang, K. R. Park, J. Kim, and Y. Lee, New focus assessment method for iris recognition systems, Pattern Recognit. Lett., vol. 29, no. 13, pp. 17591767, 2008. [21] J. R. Matey, D. Ackerman, J. Bergen, and M. Tinker, Iris recognition in less constrained environments, Springer Advances in Biometrics: Sensors, Algorithms and Systems, pp. 107131, Oct. 2007. [22] N. Kalka, J. Zuo, N. Schmid, and B. Cukic, Estimating and fusing quality factors for iris biometric, IEEE Trans. Syst., Man, Cybern. A, Syst., Humans, vol. 40, no. 3, pp. 509524, May 2010. [23] B. J. Kang and K. R. Park, A study on iris image restoration, in Proc. Int. Conf. Audio- and Video-Based Biometric Person Authentication, 2005, pp. 3140. [24] B. J. Kang and K. R. Park, A robust eyelash detection based on iris focus assessment, Pattern Recognit. Lett., vol. 28, no. 13, pp. 16301639, 2007. [25] B. J. Kang and K. R. Park, A new multi-unit iris authentication based on quality assessment and score level fusion for mobile phones, Mach. Vis. Appl., 2009, DOI: 10.1007/s00138-009-0184-0. [26] E. Krichen, S. Garcia-Salicetti, and B. Dorizzi, A new probabilistic iris quality measure for comprehensive noise detection, in Proc. IEEE Int. Conf. Biometrics: Theory, Applications, and Systems, 2007, pp. 16. [27] X. Liu, K. W. Bowyer, and P. J. Flynn, Experiments with an improved iris segmentation algorithm, in Proc. Fourth IEEE Workshop on Automatic Identication Advanced Technologies, Oct. 2005, pp. 118123. [28] G. Lu, J. Qi, and Q. Liao, A new scheme of iris image quality assessment, in Proc. Third Int. Conf. International Information Hiding and Multimedia Signal Processing, 2007, vol. 1, pp. 147150. [29] P. Meredith and T. Sarna, The phisical and chemical properties of eumelanin, Pigment Cell Res., vol. 19, pp. 572594, 2006. [30] K. Nandakumar, Y. Chen, A. K. Jain, and S. Dass, Quality based score level fusion in multibiometric systems, in Proc. Int. Conf. Pattern Recognition, 2006, pp. 473476. [31] H. Proena and L. A. Alexandre, A method for the identication of noisy regions in normalized iris images, in Proc. Int. Conf. Pattern Recognition, 2006, vol. 4, pp. 405408. [32] H. Proena, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, The UBIRIS.v2: A database of visible wavelength iris images captured on-the-move and at-a-distance, IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 8, pp. 15021516, Aug. 2010. [33] H. Proena, Iris recognition: On the segmentation of degraded images acquired in the visible wavelength, IEEE Trans. Pattern Anal. Mach. Intell., vol. 32, no. 8, pp. 15291535, Aug. 2010.
[34] H. Proena, On the feasibility of the visible wavelength, at-a-distance and on-the-move iris recognition, in Proc. IEEE Symp. Series on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications, 2009, vol. 1, pp. 915. [35] H. Proena and L. A. Alexandre, Introduction to the special issue on the segmentation of visible wavelength iris images captured at-adistance and on-the-move, Image Vis. Comput., vol. 28, no. 2, pp. 213213, 2010. [36] A. Ross, S. Crihalmeanu, L. Hornak, and S. Schuckers, A centralized web-enabled multimodal biometric database, in Proc. 2004 Biometric Consortium Conf. (BCC), Arlington, VA, Sep. 2004. [37] A. Ross and S. Shah, Iris segmentation using geodesic active contours, Special Issue on Electronic Voting, IEEE Trans. Inf. Forensics Security, vol. 4, no. 4, pp. 824836, Dec. 2009. [38] S. Schuckers, N. Schmid, A. Abhyankar, V. Dorairaj, C. Boyce, and L. Hornak, On techniques for angle compensation in nonideal iris recognition, IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 37, no. 5, pp. 11761190, Oct. 2007. [39] M. Dobes and L. Machala, UPOL Iris Database 2004 [Online]. Available: http://phoenix.inf.upol.cz/iris/ [40] J. Wan, X. He, and P. Shi, An iris image quality assessment method based on Laplacian of Gausian operation, in Proc. IAPR IAPR Conf. Machine Vision Applications, 2007, pp. 248251. [41] Z. Wei, T. Tan, Z. Sun, and J. Cui, Robust and fast assessment of iris image quality, in Proc. Int. Conf. Biometrics, 2006, pp. 464471. [42] X. Ye, P. Yao, F. Long, and Z. Zhuang, Iris image realtime pre-estimation using compound neural network, in Proc. Int. Conf. Biometrics, 2006, pp. 450456. [43] G. Zhang and M. Salganicoff, Method of Measuring the Focus of Close-Up Image of Eyes, U.S. Patent 5 953 440, 1999. [44] T. Tan, Z. He, and Z. Sun, Efcient and robust segmentation of noisy iris images for non-cooperative segmentation, Special Issue on the Segmentation of Visible Wavelength Iris Images, Elsevier Image Vis. Comput. J. , vol. 22, no. 2, pp. 223230, 2010. [45] C. T. Zahn and R. Z. Roskies, Fourier descriptors for plane closed curves, IEEE Trans. Comput., vol. C-21, no. 3, pp. 269281, Mar. 1972. [46] J. Zuo and N. Schmid, An automatic algorithm for evaluating the precision of iris segmentation, in Proc. IEEE Conf. Biometrics: Theory, Applications and Systems, 2008, pp. 16. [47] J. Zuo and N. Schmid, Global and local quality measures for NIR iris video, in Proc. Int. Conf. Computer Vision and Pattern Recognition, 2009, pp. 120125. Hugo Proena received the Ph.D. degree in computer science and engineering from the University of Beira Interior in 2007. His research interests are mainly focused in the articial intelligence, pattern recognition, and computer vision domains of knowledge, with an emphasis to the biometrics area, namely the study of iris recognition systems less constrained to subjects. Currently, he serves as Assistant Professor at the Department of Computer Science of the University of Beira Interior, Covilh, Portugal, and is with the SOCIA Lab. Soft computing and Image Analysis Group and IT-Institute of Telecommunications, Networks and Multimedia Group research groups. He is an author/coauthor of over 30 publications, in ISI-indexed International Journals and Conferences.


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Teenager Laura Dekker Sails Around the World, and in 'Maidentrip', We Get to Go Along
Maidentrip constructs a story from Laura's own footage, her views of the ocean and boat, and also from her stops on land, where she meets assorted officials, shopkeeps, and fellow travelers.
Cows. The shot of cows in Maidentrip is a familiar sort, a herd of brown and black and white animals lying in a green-green pasture, viewed from a distance, a barn in the far background. The shot is also brief, one of several used to introduce Den Osse, the village in Zeeland, Netherlands, where Laura Dekker grew up. The others include images of boats, a long shot of the Den Osse harbor, a slightly closer shot of Laura's own Hurley 800, which she's named the Guppy, and then another of Laura and her father Dick, looking down on them as they work below deck, his back tanned and taut, her shorts pink.
Laura goes on to offer a tour of the 38-foot yacht, which is to say, she gestures toward the one room where she will be living for the 17 months of her voyage around the world: "This is just the table and where you live," she says, "This will be my bed." The camera is close on her back, necessarily, because the space is so tight. Her interest in this adventure has to do with her background, she explains, her father being a Dutch boat builder ("I grew up in a shipyard with my dad building boats," she notes by way of explanation). Even getting to this point, the preparations, Laura has faced opposition. "In Holland, they try to stop her," Dick says, wiping dishes alongside his daughter at the sink, "They try to break Laura down. But Laura is actually too strong, so it's not strong enough."
And with this, Maidentrip is done with the the 10 months of legal efforts to stop Laura Dekker from becoming the youngest person ever to circumnavigate the globe. What follows is a record of her 27,000-mile trip, begun in August 2010. Directed by Jillian Schlesinger and edited by Penelope Falk, the film constructs a story from Laura's own footage, her views of the ocean and boat, her video-diary-like commentary, and also from her stops on land, where she meets assorted officials, shopkeeps, and fellow travelers.
For the first, Laura films and contemplates the world before her, the water, the wind, and the creatures. Laura's experience and the film find expression repeatedly in what she films, in spectacular images of blue or dark skies, waves crashing or, more often, Laura's announcement that a storm is coming and so she has to turn off the camera. On one level, such decisions leave off-screen the kinds of scenes you've seen in found-footagey fiction films, but on another, it lays out a more reasoned, intelligent, and practical approach to surviving a squall. Approaching the Cape of Storms (better known today as the Cape of Good Hope), Laura appears in night-vision-lens green frame, poised and determined as she tells her camera goodbye so she can pursue the business of the storm. A few moments later in the film, the sea is calm again, and she's philosophical: "I wanted to know what it felt like to be on the sea and now I know."
Being on the sea is not only being alone. Laura recounts a number of encounters, bits of punctuation on the business of sailing. Sometimes these moments are loosely metaphorical, visible indications of her internal state. Seeing dolphins alongside the Guppy near the start of her trip, Laura sniffles just a little and hopes they'll "swim with me for a while, they're a bit of company." Months later, she encounters a gull on the deck. Whether it's injured or seeking respite is unclear, but as she crouches near it, the camera positioned behind her, she turns to look back at you, noting, "I'm only speaking English to him because he probably doesn't understand Dutch."
It's a joke that speaks to Laura's evolving perception of the complex and arbitrary arrangements of nations on the globe: the meeting with the bird occurs some time after she's decided to replace her Dutch flag with one representing New Zealand, her birthplace. She doesn't take this decision lightly, thinking through what it means to be from somewhere, and to have a place to which she might return. "I don't have a home," she says, "Home to me is Guppy."
Her search for a home -- and her realization that home might be where and what she doesn't expect -- is a function of multiple factors, some of articulated, others hovering. Her parents divorced when she was young, and over the course of her voyage, during a stopover at St Maarten, she visits with her mother Babs Müller and Laura's younger sister Kim (who, Laura notes, "grew up with my mother"). Noting, "They were on vacation when I left so they didn't even wave me out," Laura records them as they play with slingshots on a pier, a scene at once joyful and weighty: "I'm nuts, yeah," smile Babs, "Or I wouldn't have a daughter sailing around the world!"
Thankful and thrilled as they all may be for this reunion, Laura comes to understand herself in relation to her own family and her own sense of home, she discovers relationships with new friends. "People ask me," she says, what she might miss from home. Working on her first pot of popcorn, the camera placed on a table to observe, she says she misses "having a bath that's not salty and moving," but as she got used to it after a coupe of weeks, "It's really normal now." Laura's changing conception of what might be "normal", either in her experience or someone else's, finds a striking correlative in her engagement with Mike and Dina, a couple of sailors she meets in Panama. They bike and hike and eat together, their community heartfelt and shared.
And yet, Laura knows, "When you meet other sailors, you already know that someday you move in a different way. It's like, normal. It's just like that, everyone does that." It doesn't reduce the meaning of such an encounter, or the feelings that might be shared. It is, as Laura comes to see, a part of growing up, of becoming independent, to recognize connections. As Laura's voiceover offers this meditation, the film shows her shadow-puppet birds against the wall of her cabin. "I have a really good friendship with my boat," she says, "It's my everything. And I hope that I have it for a long time." As Maidentrip ensures that she will, it also helps her to share that process.

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Kamakshi S
14th Sep 2012
Dark clouds have been looming over Samsung for a while now, and its PR machinery has just made it worse. Two Indian bloggers were invited to Samsung's event at the IFA, as part of the Samsung Mob!lers program. Even though they had reportedly made it clear that they would attend the event as independent journalists, they were asked to report for an outfit (uniform) fitting and promotional duties at Samsung's booth. When they refused to be downgraded to company promoters, they were threatened with a cancellation of their return tickets. You can read about the sordid saga here.
For those who aren't familiar with the Mob!lers program, it's Samsung's way of letting tech independent bloggers and enthusiasts get their hands on the latest gadgets — in the hope of generating fanboy-style reports and raves. Participants are either reporters or promoters, not both; here are some details about how the program is drafted.
Looking at the conditions imposed — such as having to wear branded T-shirts throughout the event — Clinton Jeff and his friend realised something wrong was going on. A blogger who agrees to wear the Samsung logo is deduced to be a fanboy. When the two decided to take a stand and not agree to be promoters, their return tickets and hotel accommodation were cancelled, leaving them stranded in Germany. Once the story broke though, the company duly apologized. In fact, a lot of people worldwide did read the story. They blogged, liked, shared, and tweeted about it. Samsung's reputation took a nosedive at an unfortunate point, with this happening at about the same time as the multiple Apple lawsuits.
But this isn't the first time Samsung has experienced such a foot-in-the-mouth moment. A French blogger had been asked to work as a mouthpiece for Samsung at the London 2012 Olympics. Despite having described the incident as a "30 years child in a labour camp with useless activities", she later downplayed the entire episode. I guess it's hard to convince fans that their favourite brand can do something wrong!
Maybe what Samsung did isn't so different from the norm in the area of product promotion (or maybe it is; I wouldn't know). But considering the timing, it's tempting to say the company is going through a phase of bad PR. In fact, the timing (Apple's accusations, and the launch of the GALAXY Note II) is so bad that some people have gone the way of conspiracy theories: were the bloggers paid to create negative publicity, were they Apple agents creating a misunderstanding, and so on. Regardless of the reason, you can be sure that the Korean manufacturer will be extra careful in future while recruiting "promoters".
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