Face recognition using Histogram of co-occurrence Gabor phase patterns

The fusion of Local Binary Patterns (LBP) and Gabor magnitude features has been demonstrated to be one of the most successful descriptors for face recognition. Recently, several Gabor phase based features like Histogram of Gabor Phase Patterns (HGPP) and Local Gabor XOR Patterns (LGXP) also show competitive results and complementary attributes to Gabor magnitude based features. However, in these two typical Gabor phase based approaches only the binary relationship between neighboring Gabor phases is used, which may lose some discriminative information. To investigate the potential of Gabor phase features for robust face recognition, this paper proposes a novel local descriptor, named Histogram of Co-occurrence Gabor Phase Patterns (HCGPP). In HCGPP, Gabor Phase features are first extracted and quantized into different ranges. Second we estimate the histograms of cooccurrence Gabor phase patterns in each face region. Finally, a nearest-neighbor classifier with the dissimilarity measure χ2 is used for classification. Extensive experimental results on FERET and AR databases show the significant advantages of the proposed method over the state-of-the art ones in terms of recognition rate.