Automatic Gender Recognition Using Fusion of Facial Strips

We propose a fully automatic system that detects and normalizes faces in images and recognizes their genders. To boost the recognition accuracy, we correct the in-plane and out-of-plane rotations of faces, and align faces based on estimated eye positions. To perform gender recognition, a face is first decomposed into several horizontal and vertical strips. Then, a regression function for each strip gives an estimation of the likelihood the strip sample belongs to a specific gender. The likelihoods from all strips are concatenated to form a new feature, based on which a gender classifier gives the final decision. The proposed approach achieved an accuracy of 88.1% in recognizing genders of faces in images collected from the World-Wide Web. For faces in the FERET dataset, our system achieved an accuracy of 98.8%, outperforming all the six state-of-the-art algorithms compared in this paper.