Color local phase quantization (CLPQ)- A new face representation approach using color texture cues

In this paper, we introduce new methods to encode color local texture features for enhanced face representation. In particular, we first propose a novel descriptor; color local phase quantization (CLPQ), which incorporates (channel-wise) unichrome and (cross channel) opponent features in frequency domain. Furthermore, we extend the CLPQ descriptor to multiple scales i.e. multiscale color LPQ (MS-CLPQ), which exploits the complementary information in different scales. In addition, we extend the multispectral LBP to multiple scales and propose multiscale color LBP (MS-CLBP), which provides illumination invariance and extracts features in spatial domain. To formulate the proposed color local texture descriptors, the unichrome and opponent features are combined using image-level fusion strategy and final representation of the descriptors is obtained using concatenation of regional histograms. To reduce high dimensionality of features, we applied Direct LDA, which also enhances the discrimination ability of the descriptors. The experimental analysis illustrates that proposed MS-CLPQ approach significantly outperforms other descriptor based approaches for face recognition (FR) and score level fusion of MS-CLPQ and MS-CLBP further improves the FR performance and robustness. The validity of the proposed approaches is ascertained by providing comprehensive comparisons on three challenging face databases; FRGC 2.0, GTDB and PUT.