Machine Perception In Gender Recognition Using RGB-D Sensors

Automatic gender recognition, from face images, plays an important role in various biometric applications. This task has attracted the interest of not only computer vision researchers, but also of many psychologists. Inspired by the psychological results for human gender perception. There are two main purposes for this work. First; it aims at finding out which facial parts are most effective at making the difference between men and women. Second; it tries to combine the decisions of these parts using a voting system to improve the recognition quality. Recently, with the appearance of depth sensing technology; especially the low cost devices such as the Microsoft kinect; high quality images containing color and depth information can easily be acquired. This gives us the opportunity to combine depth information with standard vision systems in order to offer a better recognition quality. In this paper, we propose an approach for classifying gender using RGB-D data based on the separation of facial parts. The experimental results show that the proposed approach improves the recognition accuracy for gender classification.