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A Study of Hand Back Skin Texture Patterns for Personal Identification and Gender Classification
Biometrics Research Center, Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong
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Received: 17 May 2012; in revised form: 14 June 2012 / Accepted: 18 June 2012 / Published: 26 June 2012
Abstract: Human hand back skin texture (HBST) is often consistent for a person and distinctive from person to person. In this paper, we study the HBST pattern recognition problem with applications to personal identification and gender classification. A specially designed system is developed to capture HBST images, and an HBST image database was established, which consists of 1,920 images from 80 persons (160 hands). An efficient texton learning based method is then presented to classify the HBST patterns. First, textons are learned in the space of filter bank responses from a set of training images using the -minimization based sparse representation (SR) technique. Then, under the SR framework, we represent the feature vector at each pixel over the learned dictionary to construct a representation coefficient histogram. Finally, the coefficient histogram is used as skin texture feature for classification. Experiments on personal identification and gender classification are performed by using the established HBST database. The results show that HBST can be used to assist human identification and gender classification.
Keywords: biometrics; hand back skin texture; texton learning; sparse representation
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Cite This Article
MDPI and ACS Style
Xie, J.; Zhang, L.; You, J.; Zhang, D.; Qu, X. A Study of Hand Back Skin Texture Patterns for Personal Identification and Gender Classification. Sensors 2012, 12, 8691-8709.
Xie J, Zhang L, You J, Zhang D, Qu X. A Study of Hand Back Skin Texture Patterns for Personal Identification and Gender Classification. Sensors. 2012; 12(7):8691-8709.
Xie, Jin; Zhang, Lei; You, Jane; Zhang, David; Qu, Xiaofeng. 2012. "A Study of Hand Back Skin Texture Patterns for Personal Identification and Gender Classification." Sensors 12, no. 7: 8691-8709.