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Statistical approaches to gender classification in the surface normal domain

Humans can readily determine the gender from most faces without much effort. This has aroused interest to investigate what are the underlying cues that differentiate males and females. Guided by psychological findings, this thesis presents research on gender classification using facial needle-maps. A facial needle-map is a field of surface normals which describes the local orientation of a facial surface. It on one hand reveals the facial shape information, on the other hand can be recovered using shape-from-shading. This thesis demonstrates the feasibility of gender classification using facial needle-maps delivered by shape-from-shading. More importantly, this thesis proposes statistical approaches to find gender discriminating features from facial needle-maps, and shows that the use of these features improves the gender classification performance. In Chapter 2, we thoroughly review the relevant literature of gender classification in psychology and computer vision, and the approaches for facial shape recovery. Guided by humans recognition of gender, we notice that in the literature of automatic gender classification, little effort has been put into using the 3D shape information conveyed by facial surfaces for gender classification, or taking into account the nonuniform distribution of gender information over facial regions. This thesis addresses these weaknesses, and fills a gap in the gender classification literature. Chapter 3 presents our first attempt to perform gender classification using facial needle-maps which are recovered using shape-from-shading. We describe the principal statistical tools, namely principal geodesic shape-from-shading (PGSFS), and principal geodesic analysis (PGA), for the recovery and representation of facial needle-maps, and examine the classification results which are obtained through a straightforward use of the recovered needle-maps. The results on one hand demonstrate the feasibility of gender classification using shape-from-shading, on the other hand motivate us to find gender discriminating features to improve the classification performance. In Chapter 4, we find that the gender discriminating capacity is not uniform for each eigenmode in the PGA feature space. We therefore explore two strategies to select the gender discriminating feature subset from the PGA feature vector representation of facial needle-maps. The first strategy is based on a supervised method making use of criterion functions and search algorithms. The second is based on a semi-supervised method which incorporates graph embedding. We develop the two strategies based on range data, and then apply them to facial needle-maps recovered using shape-from-shading. A short-runs EM algorithm and a Gaussian mixture model are applied to the selected features of both types of data, and the results demonstrate the gender separability of the selected feature subsets. In Chapter 5, we take into account the non-uniform distribution of gender information over facial regions, and extract gender discriminating features by incorporating gender sensitive weight maps. Three novel variants of PGA, namely a) weighted PGA, b) supervised weighted PGA, and c) supervised PGA, are proposed. In these methods, we show how the weight maps are constructed, and how they are incorporated into the construction of gender discriminating models, and improve the gender discriminating capacity of the extracted features. We apply our methods to both range data and facial needle-maps recovered using shape-from-shading. The results demonstrate the effectiveness of our methods for feature extraction, and show the improved performance of gender classification on both types of data. We summarize the contributions of this thesis in Chapter 6, and analyze the developed methods. Finally, we give some suggestions to the future work in automatic gender classification.