Patch based local phase quantization of monogenic components for face recognition

In this paper, we propose a novel feature extraction method for Face recognition called patch based Local Phase Quantization of Monogenic components (PLPQMC). From the input image, the directional Monogenic bandpass components are generated. Then, each pixel of a bandpass image is replaced by the mean value of its rectangular neighborhood. Next, LPQ histogram sequences are computed upon those images. Finally, these histogram sequences are concatenated for constituting a global representation of the face image. Using the proposed method for feature extraction, we construct a new face recognition system with Whitened Principal Component Analysis (WPCA) for dimensionality reduction, k-nearest neighbor classifier and weighted angle distance for classification. Performance evaluations on two public face databases FERET and SCface show that our method is efficient against some challenging issues, e.g. expressions, illumination, time-lapse, low resolution, and it is competing with state-of-the-art methods.