Infrared face recognition based on co-occurrence histogram of multi-scale local binary patterns

Different scales local binary patterns (LBP) extract different micro-structures, which contain important discriminative information for infrared face recognition. To capture the correlation between different scales, a new infrared face recognition method based on multi-scale LBP co-occurrence histogram was proposed in this paper. In traditional multi-scale LBP-based features, correlation in different micro-structures was ignored. To consider such correlation in infrared faces, co-occurrence histogram of multi-scale LBP codes was used to represent the infrared face. Multi-scale LBP co-occurrence histogram not only preserved great invariance to environmental temperature, but also greatly enhanceed the discriminative power of the descriptor as co-occurrence matrix of LBP code well captureed the correlation between different scale micro-structures around the same central point. The experimental results show the recognition rates of infrared face recognition method based on multi-scale LBP co-occurrence histogram can reaches 99.2% under same condition and 91.2% under variable ambient temperatures, outperform that of the classic methods based on LBP and multi-scale LBP histogram.