Deep learning for gender recognition

The secondary sex characteristics in faces of people are quite different due to variations of age, sex hormone, race and dress-up style. It is a very challenging work to build a gender recognition model for all kinds of people. This paper proposes to train a gender recognition model based on the deep convolutional network on a complete dataset. Our newly built complete dataset contains as many common variations of face images as possible. Based on this complete dataset, we design a very deep convolutional network as our gender classifier. We achieve an accuracy of 98.67% on the most challenging public database, labelled faces in the wild (LFW) [1]· We collect 10000 images from Internet and build a new dataset — Chinese wild database. Our model achieves the accuracy of 97.51% This indicates our model is robust to racial variation. In the above two experiments, our model achieves the state-of-the-art performances in the wild.