Illumination invariant face recognition using Fused Cross Lattice Pattern of Phase Congruency (FCLPPC)

Abstract

This paper presents a new facial feature descriptor called Fused Cross Lattice Pattern of Phase Congruency (FCLPPC) for high accuracy, homogeneous and heterogeneous illumination invariant cross-modal face recognition. Using the dimensionless phase congruency features, an effective illumination-invariant local feature extractor has been devised. To this end, a novel multi-directional binary pattern named Cross Lattice Pattern (CLP) has been proposed. CLP is applied to the previously extracted invariant phase congruency feature maps to generate the CLPPC images. Finally, weighted alpha-blending has been performed on the CLPPC maps to generate the Fused CLPPC (FCLPPC) feature map. Recognition results on Extended Yale-B, TUFTS, CMU-PIE, and CASIA NIR-VIS datasets have been presented to depict the superiority of the proposed scheme over other state-of-the-art methods. Additionally, the proposed FCLPPC has been combined with a lightweight Convolutional Neural Network to further augment the recognition accuracy.