Locally adaptive texture features for multispectral face recognition

Abstract

This work introduces a new locally adaptive texture features for efficient multispectral face recognition. This new descriptor called Local Adaptive Ternary Pattern (LATP) is based on the Local Ternary Pattern (LTP). Unlike the previous techniques, this new descriptor determines the local pattern threshold automatically using local statistics. It shares with LTP the property of being less sensitive to noise, illumination change and facial expressions. These characteristics make it a good candidate for multispectral face recognition. Linear and non linear subspace learning and recognition techniques are introduced and used for performance evaluation of face recognition in the new texture space: PCA, LDA, Kernel-PCA (KPCA), Kernel-LDA (KDA), Linear Graph Embedding (LGE), Kernel-LGE (KLGE), Locality Preserving Projection (LPP) and Kernel-LPP (KLPP). The obtained results show an increase in recognition performance when texture features are used. LTP and LATP are the best performing techniques. The overall best performance is obtained in the short wave infrared spectrum (SWIR) using the new proposed technique combined with a non linear subspace learning technique.