Comparison of Palm and Dorsal Hand Recognition

  • David Zhang
  • Zhenhua Guo
  • Yazhuo Gong


Palm and dorsal hand have received increasing attention during the last two decades. Though the techniques about the two biometrics started later than some other kinds such as face and fingerprint , their properties of uniqueness and permanence have helped them to own unique and stable status in biometric recognition. Strictly analyzing, the two hand-based biometrics features have lots of common characteristics. For example, the way of ROI localization is similar, and the experience can be borrowed from each other. Meanwhile, they both have surface skin texture and deep vein patterns for researching. Nevertheless, recognition performance of palm is much superior to dorsal hand. This phenomenon mainly results from two aspects, the texture difference and spectral difference. The texture difference determines the feature patterns we can extract for recognition, while the spectral difference can take an important role in image qualities. In fact, these two factors are both related to the difference of biologic tissues. Analyzing optical character of palm skin and dorsal hand skin is necessary to explain the distinguishable capability difference. A combined database of palm and dorsal hand is established with 208 volunteers. The particularity is that the database contains palm and dorsal hand images from the same person. The later recognition experiment result is more convincing to testify the superiority of palm, because the palm and dorsal images are all taken in the same time, and we can make sure that the multispectral light source, collecting environment and status of human body, cannot be interference factors in comparison. The result shows that palm has greatly lower equal error rate , no matter under whether optimal single band or optimal multiple bands.


Biometric difference analysis Skin model Combined database Biological tissue comparison 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Biometrics Research CentreThe Hong Kong Polytechnic UniversityHung HomHong Kong SAR
  2. 2.Shenzhen Key Laboratory of Broadband Network & Multimedia, Graduate School at ShenzhenTsinghua UniversityShenzhenChina
  3. 3.University of Shanghai for Science and TechnologyShanghaiChina

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