Robust gender classification using a precise patch histogram

This study proposed a precise facial feature extraction method to improve the accuracy of gender classification under pose and illumination variations. We used the active appearance model (AAM) to align the face image. Images were modeled by the patches around the coordinates of certain landmarks. Using the proposed precise patch histogram (PPH) enabled us to improve the accuracy of the global facial features. The system is composed of three phases. In the training phase, non-parametric statistics were used to describe the characteristics of the training images and to construct the patch library. In the inference phase, the choice of feature patch from the library needed to approximate the patch of the testing image was based on the maximum a posteriori estimation. In the estimation phase, a Bayesian framework with portion-oriented posteriori fine-tuning was employed to determine the classification decision. In addition, we developed the dynamic weight adaptation to obtain a more convincing performance. The experimental results demonstrated the robustness of the proposed method.