Local-Gravity-Face (LG-face) for Illumination-Invariant and Heterogeneous Face Recognition

Abstract

This paper proposes a novel method called local-gravity-face ( LG-face ) for illumination-invariant and heterogeneous face recognition (HFR). LG-face employs a concept called the local gravitational force angle ( LGFA ). The LGFA is the direction of the gravitational force that the center pixel exerts on the other pixels within a local neighborhood. A theoretical analysis shows that the LGFA is an illumination-invariant feature, considering only the reflectance part of the local texture effect of the neighboring pixels. It also preserves edge information. Rank 1 recognition rates of 97.78% on the CMU-PIE database and 97.31% on the Extended Yale B database are achieved under varying illumination, demonstrating that LG-face is an effective method of illumination-invariant face recognition. For HFR, when faces appear in different modalities, LG-face produces a common feature representation. Rank 1 recognition rates of 99.96% on the CUFS database, 98.67% on the CUFSF database, and 99.78% on the CASIA-HFB database show that the LG-face is also an effective method for HFR. The proposed method also performs consistently in the presence of complicated variations and noise.