Local binary pattern (LBP) and local phase quantization (LBQ) based on Gabor filter for face representation

Sometimes realistic face representation is confronted with blur or low-resolution face images, as a result, existing classification methods are not powerful and robust enough. This paper proposes a novel face representation approach (GLL) which fuses Gabor filter, Local Binary Pattern (LBP) and Local Phase Quantization (LPQ). In the process of Gabor filter, it uses Gabor wavelet functions with two scales and eight orientations to capture the salient visual properties in face image. On this basis of Gabor features, we acquire LBP features and LPQ features, respectively, so as to fully explore the blur invariant property and the information in the spatial domain and among different scales and orientations. Experiments on both CMU-PIE and Yale B demonstrate the effectiveness of our GLL when dealing with different condition face data sets.