Abstract
For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks' information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples' own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system.
Highlights
Today, biometric recognition has become a common and reliable way to authenticate the identity of a living person based on physiological or behavioral characteristics
Broussard et al [1] pointed out that the notable trait of partial iris recognition algorithms is the inner regions of iris which produce much less identification accuracy than the center or outer regions do; this does not indicate that inner regions of iris do not contribute to the accuracy of the entire template; it means that there is less stable, discriminatory information that existed in the inner iris regions
We propose a feature information fusion scheme for different portions of iris on the basis of local quality evaluation
Summary
Biometric recognition has become a common and reliable way to authenticate the identity of a living person based on physiological or behavioral characteristics. Because of its uniqueness and stability, iris recognition is one of the most reliable human identification techniques. Most iris recognition systems require a cooperative subject; capturing the entire iris may be infeasible in surveillance application. Partial iris recognition algorithms are going to play a significant role. Broussard et al [1] pointed out that the notable trait of partial iris recognition algorithms is the inner regions of iris which produce much less identification accuracy than the center or outer regions do; this does not indicate that inner regions of iris do not contribute to the accuracy of the entire template; it means that there is less stable, discriminatory information that existed in the inner iris regions
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