Face Recognition using Multi-Scale Local Phase Quantisation and Linear Regression Classifier

Linear Regression Classifier (LRC) is state-of-the-art face recognition method that represent a probe image as a linear combination of class specific models. However, this method views the image as a point in a feature space, and thus LRC cannot accommodate severe luminance alterations. Histogram-based features, such as Multiscale Local Phase Quantisation histogram (MLPQH) have gained reputation as powerful and attractive texture descriptors showing excellent results in terms of accuracy and computational complexity in face recognition. In this paper, MLPQH features are integrated with ``face'' features to confront the illumination problem in LRC. The main novelty is the fusion of histogram and face features using {\it z}-score normalisation and LRC classifier. The proposed system is evaluated on two benchmarks: ORL and Extended Yale B. The results indicate a significant increase in the performance when compared with state-of-the-art face recognition methods.