The relative distance of key point based iris recognition

  • Authors:

  • Li Yu;David Zhang;Kuanquan Wang

  • Affiliations:

  • School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin 150001, China;Biometric Research Center, Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong;School of Computer Science and Technology, Harbin Institute of Technology (HIT), Harbin 150001, China

  • Venue:
  • Pattern Recognition
  • Year:
  • 2007

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Abstract

Iris recognition has received increasing attention in recent years as a reliable approach to human identification. This paper makes an attempt to analyze the local feature structure of iris texture information based on the relative distance of key points. When preprocessed, the annular iris is normalized into a rectangular block. Multi-channel 2-D Gabor filters are used to capture the iris texture. In every filtered sub-image, we extract the points that can represent the local texture most effectively in each channel. The barycenter of these points in each channel is called the key point and a group of key points are obtained. Then, the distance between the center of key points of each sub-image and every key point is called relative distance, which is regarded as the iris feature vector. Iris feature matching is based on the Euclidean distance. Experimental results on public and private databases show that the performance of the proposed method is encouraging.