Fusing Magnitude and Phase Features for Robust Face Recognition

Abstract

High accurate face recognition is of great importance for real-world applications such as identity authentication, watch list screening, and human-computer interaction. Despite tremendous progress made in the last decades, fully automatic face recognition systems are still far from the goal of surpassing the human vision system, especially in uncontrolled conditions. In this paper, we propose an approach for robust face recognition by fusing two complementary features: one is the Gabor magnitude of multiple scales and orientations and the other is Fourier phase encoded by Local Phase Quantization (LPQ). To further reduce the high dimensionality of both features, patch-wise Fisher Linear Discriminant Analysis is applied respectively and further combined by score-level fusion. In addition, multi-scale face models are exploited to make use of more information and improve the robustness of the proposed approach. Experimental results show that the proposed approach achieves 96.09%, 95.64% and 95.15% verification rates (when FAR=0.1%) on ROC1/2/3 of Face Recognition Grand Challenge (FRGC) version 2 Experiment 4, impressively surpassing the best known results, i.e. 93.91%, 93.55%, and 93.12%.