Exploiting effective facial patches for robust gender recognition

Gender classification is an important task in automated face analysis. Most existing approaches for gender classification use only raw/aligned face images after face detection as input. These methods exhibit fair classification ability under constrained conditions, in which face images are acquired under similar illumination with similar poses. The performances of these methods may deteriorate when face images show drastic variances in poses and occlusion as routinely encountered in real-world data. The reduction in the performances of current gender classification methods may be attributed to the sensitiveness of features to image translations. This work proposes to alleviate this sensitivity by introducing a majority voting procedure that involves multiple face patches. Specifically, this work utilizes a deep learning method based on multiple large patches. Several Convolutional Neural Networks (CNN) are trained on individual, predefined patches that reflect various image resolutions and partial cropping. The decisions of each CNN are aggregated through majority voting to obtain the final gender classification accurately. Extensive experiments are conducted on four gender classification databases, including Labeled Face in-the-Wild (LFW), CelebA, ColorFeret, and All-Age Faces database, a novel database collected by our group. Each individual patch is evaluated, and complementary patches are selected for voting. We show that the classification accuracy of our method is comparable with that of state-of-the-art systems. This characteristic validates the effectiveness of our proposed method.