Learning Gabor magnitude features for palmprint recognition

  • Authors:

  • Rufeng Chu;Zhen Lei;Yufei Han;Ran He;Stan Z. Li

  • Affiliations:

  • Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences;Center for Biometrics and Security Research & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences

  • Venue:
  • ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part II
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

Palmprint recognition, as a new branch of biometric technology, has attracted much attention in recent years. Various palmprint representations have been proposed for recognition. Gabor feature has been recognized as one of the most effective representations for palmprint recognition, where Gabor phase and orientation feature representations are extensively studied. In this paper, we explore a novel Gabor magnitude feature-based method for palmprint recognition. The novelties are as follows: First, we propose an illumination normalization method for palmprint images to decrease the influence of illumination variations caused by different sensors and lighting conditions. Second, we propose to use Gabor magnitude features for palmprint representation. Third, we utilize AdaBoost learning to extract most effective features and apply Local Discriminant Analysis (LDA) to reduce the dimension further for palmprint recognition. Experimental results on three large palmprint databases demonstrate the effectiveness of proposed method. Compared with state-of-the-art Gabor-based methods, our method achieves higher accuracy.