Palmprint recognition using 3-D information

  • Authors:
  • David Zhang;Guangming Lu;Wei Li;Lei Zhang;Nan Luo

  • Affiliations:
  • Biometrics Research Center, Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Biocomputing Research Center, Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China;Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, China;Biometrics Research Center, Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong;Biometrics Research Center, Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
  • Year:
  • 2009

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Abstract

Palmprint has proved to be one of themost unique and stable biometric characteristics. Almost all the current palmprint recognition techniques capture the 2-D image of the palm surface and use it for feature extraction andmatching. Although 2-D palmprint recognition can achieve high accuracy, the 2-D palmprint images can be counterfeited easily and much 3-D depth information is lost in the imaging process. This paper explores a 3-D palmprint recognition approach by exploiting the 3-D structural information of the palm surface. The structured light imaging is used to acquire the 3-D palmprint data, from which several types of unique features, including mean curvature image, Gaussian curvature image, and surface type, are extracted. A fast feature matching and score-level fusion strategy are proposed for palmprint matching and classification. With the established 3-D palmprint database, a series of verification and identification experiments is conducted to evaluate the proposed method. The results demonstrate that 3-D palmprint technique has high recognition performance. Although its recognition rate is a little lower than 2-D palmprint recognition, 3-D palmprint recognition has higher anticounterfeiting capability and is more robust to illumination variations and serious scrabbling in the palm surface. Meanwhile, by fusing the 2-D and 3-D palmprint information, much higher recognition rate can be achieved.