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A Wavelet-based Feature Selection Scheme for Palm-print Recognition

In this paper, a multi-resolution feature extraction algorithm for palm-print recognition is proposed based on two-dimensional discrete wavelet transform (2D-DWT), which efficiently exploits the local spatial variations in a palmprint image. The entire image is segmented into several small spatial modules and a palm-print recognition scheme is developed, which extracts histogram-based dominant wavelet features from each of these local modules. This not only drastically reduces the feature dimension but also results in a very high within-class compactness and between-class separability of the extracted features. Moreover, the improvement of the quality of the extracted features as a result of illumination adjustment has also been analyzed. A principal component analysis is performed to further reduce the feature dimension. From our extensive experimentations on different palmprint databases, it is found that the performance of the proposed method in terms of recognition accuracy and computational complexity is superior to that of some of the recent methods.