Feature Band Selection for Online Multispectral Palmprint Recognition

  • David Zhang
  • Zhenhua Guo
  • Yazhuo Gong


Palmprint is a unique and reliable biometric feature with high usability. In the past decades, many palmprint recognition systems have been successfully developed. However, most of previous works use the white light as the illumination source, and the recognition accuracy and anti-spoof capability are limited. Recently, multispectral imaging attracts research attention as it can acquire more discriminative information in a short time. One crucial step in developing online multispectral palmprint systems is how to determine the optimal number of spectral bands and select the most representative bands to build the system. This chapter presents a study on feature band selection by analyzing hyperspectral palmprint data (520–1050 nm). Our experimental results showed that three spectral bands could provide most of discriminate information of palmprint. This finding could be used as the guidance for designing new online multispectral palmprint systems.


Multispectral palmprint recognition Clustering Biometrics Anti-spoof 


  1. Boyce C, Ross A, Monaco M, Hornak L, Li X (2006) Multispectral iris analysis: a preliminary study. In: IEEE computer society conference on computer vision and pattern recognition. Workshops, pp 51–59Google Scholar
  2. Chang H, Yao Y, Koschan A, Abidi B, Abidi M (2008) Spectral range selection for face recognition under various illuminations. In: International conference on image processing, pp 2756–2759Google Scholar
  3. Chang H, Yao Y, Koschan A, Abidi B, Abidi M (2009) Improving face recognition via narrowband spectral range selection using Jeffrey divergence. IEEE Trans Inf Forensics Secur 4(1):111–122CrossRefGoogle Scholar
  4. Connie T, Andrew T, Goh K (2005) An automated palmprint recognition system. Image Vis Comput 23:501–505CrossRefGoogle Scholar
  5. Di W, Zhang L, Zhang D, Pan Q (2010) Studies on hyperspectral face recognition in visible spectrum with feature band selection. IEEE Trans Syst Man Cyberns Part A Syst Hum 40:1354—1361Google Scholar
  6. Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, BerlinGoogle Scholar
  7. Guo B, Gunn SR, Damper RI, Nelson JDB (2006) Band selection for hyperspectral image classification using mutual information. IEEE Geosci Remote Sens Lett 3:522–526CrossRefGoogle Scholar
  8. Guo Z, Zhang L, Zhang D (2010) Feature band selection for multispectral palmprint recognition. In: International conference on pattern recognition, pp 1136–1139Google Scholar
  9. Han C, Cheng H, Lin C, Fan K (2003) Personal authentication using palm-print features. Pattern Recogn 36:371–381CrossRefGoogle Scholar
  10. Hao Y, Sun Z, Tan T (2007) Comparative studies on multispectral palm image fusion for biometrics. In: Asian conference on computer vision, pp 12–21Google Scholar
  11. Hao Y, Sun Z, Tan T, Ren C (2008) Multispectral palm image fusion for accurate contact-free palmprint recognition. In: International conference on image processing, pp 281–284Google Scholar
  12. Hu D, Feng G, Zhou Z (2007) Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition. Pattern Recogn 40:339–342MATHCrossRefGoogle Scholar
  13. Jain A, Bolle R, Pankanti S (1999) Biometrics: personal identification in network society. Kluwer, BostonCrossRefGoogle Scholar
  14. Jia W, Huang D, Zhang D (2008) Palmprint verification based on robust line orientation code. Pattern Recogn 41:1504–1513MATHCrossRefGoogle Scholar
  15. Mendenhall W, Beaver RJ, Beaver BM (2003) Probability and statistics. Thomson, Brooks/ColeGoogle Scholar
  16. Ross AA, Nadakumar K, Jain AK (2006) Handbook of multibiometrics, Springer, BerlinGoogle Scholar
  17. Rowe RK, Nixon KA, Corcoran SP (2005) Multi spectral fingerprint biometrics. In: Proceedings of information assurance workshop, pp 14–20Google Scholar
  18. Rowe RK, Uludag U, Demirkus M, Parthasaradhi S, Jain AK (2007) A multispectral whole-hand biometric authentication system. In: Proceedings of biometric symposium. Biometric consortium conference, pp 1–6Google Scholar
  19. Sampat MP, Wang Z, Gupta S, Bovik AC, Markey MK (2009) Complex wavelet structural similarity: a new image similarity index. IEEE Trans Image Process 18:2385–2401MathSciNetCrossRefGoogle Scholar
  20. Schukers SAC (2002) Spoofing and anti-spoofing measures. Inf Secur Tech Report 7:56–62CrossRefGoogle Scholar
  21. Wang H, Angelopoulou E (2006) Sensor band selection for multispectral imaging via average normalized information. J Real-Time Image Proc 1:109–121CrossRefGoogle Scholar
  22. Zhang L, Guo Z, Wang Z, Zhang D (2007) Palmprint verification using complex wavelet transform. In: International conference on image processing, pp 417–420Google Scholar
  23. Zhang D, Guo Z, Lu G, Zhang L, Zuo W (2010) An online system of multi-spectral palmprint verification. IEEE Trans Instrum Meas 59:480–490CrossRefGoogle Scholar

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

Personalised recommendations