Video gender recognition using temporal coherent face descriptor

In this paper, we propose a new temporal coherent face descriptor for video gender recognition. The proposed face descriptor is constructed from detected faces of continuous video frames. Because it describes detected faces under variant changes in continuous video frames and provides a unified feature description, face normalization and alignment processes can be avoided during gender recognition. Based on the face descriptor, a support vector machine classifier is applied to identify the gender of the subject in the videos. As shown in the experiments, our method not only achieves better results compared to the state-of-the-art methods but also the real-time performance for video processing.