A study of hand back skin texture patterns for personal identification and gender classification

Jin Xie, Lei Zhang, Jia You, Dapeng Zhang, Xiaofeng Qu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

15 Citations (Scopus)


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 l1-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.
Original languageEnglish
Pages (from-to)8691-8709
Number of pages19
JournalSensors (Switzerland)
Issue number7
Publication statusPublished - 1 Jul 2012


  • Biometrics
  • Hand back skin texture
  • Sparse representation
  • Texton learning

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering


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