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Proceedings Paper

Information distance-based selective feature clarity measure for iris recognition
Author(s): Craig Belcher; Yingzi Du
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Paper Abstract

Iris recognition systems have been tested to be the most accurate biometrics systems. However, poor quality images greatly affect accuracy of iris recognition systems. Many factors can affect the quality of an iris image, such as blurriness, resolution, image contrast, iris occlusion, and iris deformation, but blurriness is one of the most significant problems for iris image acquisition. In this paper, we propose a new method to measure the blurriness of an iris image called information distance based selective feature clarity measure. Different from any other approach, the proposed method automatically selects portions of the iris with most changing patterns to measure the level of blurriness based on their frequency characteristics. Log-Gabor wavelet is used to capture the features of the selected portions. By comparing the information loss from the original features to blurred versions of the same features, the algorithm decides the clarity of the original iris image. The preliminary experiment results show that this method is effective.

Paper Details

Date Published: 29 January 2007
PDF: 12 pages
Proc. SPIE 6494, Image Quality and System Performance IV, 64940E (29 January 2007); doi: 10.1117/12.704702
Show Author Affiliations
Craig Belcher, Indiana Univ.-Purdue Univ. Indianapolis (United States)
Yingzi Du, Indiana Univ.-Purdue Univ. Indianapolis (United States)


Published in SPIE Proceedings Vol. 6494:
Image Quality and System Performance IV
Luke C. Cui; Yoichi Miyake, Editor(s)

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