Information entropy-based vein recognition for two-dimensional local binary patterns
Based on the problem that the existing LBP algorithm and its variants are unable to extract high-dimensional fea-tures from images,a two-dimensional local binary pattern recognition algorithm based on information entropy is proposed.This method first extracts the low-dimensional features of the image using the unified local binary pattern(ULBP),then combines the im-age information entropy with the unified local binary pattern atlas to obtain the entropy-weighted unified local binary pattern atlas(EULBP),and realizes the statistics of the co-occurring feature information among the patterns in the local area using the sliding window,and uses the result as the image feature expression.And the pattern classifier constructed on the basis of histogram cross distance is used to verify its recognition performance.The experimental results show that the proposed algorithm can achieve an av-erage recognition rate of 99.94%and 98.84%in both the SDUMLA-HMT dataset as well as the Universiti Teknologi Malaysia fin-ger vein dataset(FV-USM).
two-dimensional co-occurrence of local binary patternsinformation entropyrotational invariancedirectional features
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