Abstract
Infrared imaging can acquire the intrinsic temperature information of the skin, which is robust to the impacts of illumination conditions and disguises. This paper proposes an improved infrared face recognition method based on local binary pattern (LBP). To extract the local robust features in infrared face images, LBP is chosen to get the composition of micro-patterns of sub-blocks. Based on statistical test theory, Kruskal-Wallis (KW) feature selection method is proposed to get the LBP patterns which are suitable for infrared face recognition. The experimental results show combination of LBP and KW features selection improves the performance of infrared face recognition, the proposed method outperforms the traditional methods based on LBP histogram, discrete cosine transform(DCT) or principal component analysis(PCA).