Quantized fuzzy LBP for face recognition
Face recognition under large illumination variations is challenging. Local binary pattern (LBP) is robust to illumination variation, but sensitive to noise. Fuzzy LBP (FLBP) partially solves the noise-sensitivity problem by incorporating fuzzy logic in the representation of local binary patterns. The fuzzy membership function is determined by both sign and magnitude of the pixel difference. However, the magnitude is easily altered by noise, hence could be unreliable. Thus, we propose to determine the fuzzy membership function by its sign only. We name the proposed approach as Quantized Fuzzy LBP (QFLBP). On two challenging face recognition datasets, it is shown more robust to noise, and demonstrates a superior performance to FLBP and many other LBP variants.