中国邮电高校学报(英文) ›› 2008, Vol. 15 ›› Issue (1): 96-101.doi: 1005-8885 (2008) 01-0096-06

• Artificial Intelligence • 上一篇    下一篇

An image segmentation based method for iris feature extraction

徐光柱;马义德;张在峰   

  1. The College of Electrical Engineering and Information Technology,
    China Three Gorges University, Yichang 443002, China
  • 收稿日期:2007-04-17 修回日期:1900-01-01 出版日期:2008-03-31
  • 通讯作者: 徐光柱

An image segmentation based method for iris feature extraction

  1. The College of Electrical Engineering and Information Technology,
    China Three Gorges University, Yichang 443002, China
  • Received:2007-04-17 Revised:1900-01-01 Online:2008-03-31
  • Contact: Xu Guangzhu

摘要:

In this article, the local anomalistic blocks such as crypts, furrows, and so on in the iris are initially used directly as iris features. A novel image segmentation method based on intersecting cortical model (ICM) neural network was introduced to segment these anomalistic blocks. First, the normalized iris image was put into ICM neural network after enhancement. Second, the iris features were segmented out perfectly and were output in binary image type by the ICM neural network. Finally, the fourth output pulse image produced by ICM neural network was chosen as the iris code for the convenience of real time processing. To estimate the performance of the presented method, an iris recognition platform was produced and the Hamming Distance between two iris codes was computed to measure the dissimilarity between them. The experimental results in CASIA v1.0 and Bath iris image databases show that the proposed iris feature extraction algorithm has promising potential in iris recognition.

关键词:

iris;recognition,;image;segmentation,;ICM

Abstract:

In this article, the local anomalistic blocks such as crypts, furrows, and so on in the iris are initially used directly as iris features. A novel image segmentation method based on intersecting cortical model (ICM) neural network was introduced to segment these anomalistic blocks. First, the normalized iris image was put into ICM neural network after enhancement. Second, the iris features were segmented out perfectly and were output in binary image type by the ICM neural network. Finally, the fourth output pulse image produced by ICM neural network was chosen as the iris code for the convenience of real time processing. To estimate the performance of the presented method, an iris recognition platform was produced and the Hamming Distance between two iris codes was computed to measure the dissimilarity between them. The experimental results in CASIA v1.0 and Bath iris image databases show that the proposed iris feature extraction algorithm has promising potential in iris recognition.

Key words:

iris recognition;image segmentation;ICM

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