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Multiple Band Selection of Multispectral Dorsal Hand

  • David Zhang
  • Zhenhua Guo
  • Yazhuo Gong
Chapter

Abstract

The optimal single band for dorsal hand recognition is 890 nm based on the fusion of MFRAT and CompCode. The extracted vein information is limited within one spectrum only. One of the advantages of multispectral technique is to pursue higher recognition performance through the fusion of multiple bands. The main theoretical basis is that the images on different bands have complementary information, which is helpful to performance improvement. Obviously, adding more bands is similar to feature dimension increase, and the redundant information may also increase quickly especially when the bands are highly relevant to each other. So the multispetral band selection process is limited to high efficiency, and it demands that the selected bands have the maximum uncorrelation. Noting that recognition of visible region is obviously far worse and the texture of non-vein part is not reliable enough for efficient recognition, we plan to do the multiple band selection work in near infrared (NIR) region only. 422 dorsal hands of East Asians with different bands ranging from 700 to 1040 nm are used. This chapter tries to address two basic issues, the number of the bands for optimal group and which bands can explain the multispectral model more precisely. Unlike optimal single band selection, exhaustive method is not practicable for this task. The number of possible combination is immeasurable, especially when the optimal band number is unknown. Our scheme is to realize the task in two steps: First, divide the NIR region into several band classes according to special rules so that the number of optimal band is fixed; secondly, choose the bands from these classes to represent them with proper estimation criterion.

Keywords

Dorsal hand recognition Multispectral image Band clustering Band fusion 

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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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