Learning Part-Based Dictionary by Sparse NMF for Face Gender Recognition

Face gender recognition is a very challenging problem in computer vision, which plays an important role in many visual applications. In this paper, we present a framework that combines the unsupervised dictionary learning and supervised classifier training together to this gender recognition problem. We firstly apply sparse non-negative matrix factorization (sparse NMF) to learn intrinsic part-based dictionary from face images in an unsupervised manner. After that we encode all the data by the learned dictionary, and train a SVM or logistic regression classifier in a supervised manner on those representations. Our experimental results show that the learned dictionaries by sparse NMF can not only capture meaningful features from the faces, but also boost the performance of the subsequent classifier in terms of classification accuracies and speeds.