Gender classification by LPQ features from intensity and Monogenic images

Gender is one of the most useful facial attributes which are detected from human face images. In this work, we introduce a new gender classification system based on features extracted by Local Phase Quantization (LPQ) operators from intensity and Monogenic images. More detailed, the LPQ features are obtained from the input image (the intensity one) and from three other Monogenic components in the feature extraction stage. In the classification stage, we employ the binary SVM classifier to predict the gender of the given test images. The comparisons among our experimental results upon two public databases, LFW and Groups dataset, and those of other systems show that the proposed system is comparable with state-of-the-art approaches when it attains competing accuracy rates (97.0% on LFW and 91.58% upon Groups dataset).