Gabor Filtering and Adaptive Optimization Neural Network for Iris Double Recognition
The iris image is greatly affected by the collection environment, so, the outputs of different iris categories in the distance recognition algorithm may similar. Neural network recognition algorithm can improve the results distinction, but the same neural network structure has a great difference in the recognition effect of different iris libraries. They all may reduce the accuracy of iris recognition. This paper proposes an iris double recognition algorithm based on Gabor filtering and adaptive optimization neural network. Gabor filtering is used to extract iris features. Hamming distance is used to eliminate most of different categories in the first recognition. The BP neural network that connection weights are optimized by immune particle swarm optimization algorithm is used for the second recognition. The results that the proposed algorithm compares with many algorithms in different iris libraries show that the proposed algorithm can effectively improve iris recognition accuracy.
KeywordsIris double recognition Gabor filtering Adaptive optimization neural network Hamming distance Immune particle swarm optimization
The authors would like to thank the support of the National Natural Science Foundation of China (NSFC) under Grant No. 61471181. Natural Science Foundation of Jilin Province under Grant No. 20140101194JC, 20150101056JC. Science and technology project of the Jilin Provincial Education Department under Grant No. JJKH20180448KJ.
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