Age and gender recognition using informative features of various types

Gender recognition and age classification are important applications of face analysis. The vast majority of the existing solutions focus on a single visual descriptor which often encodes only a certain characteristic of the image regions (e.g., shape, or texture, or color, etc.). In this paper, we propose a novel framework for gender and age classification, which facilitates the integration of multiple feature types and therefore allows for taking advantage of various sources of visual information. Furthermore, in the proposed method, only the regions that can best separate face images of different demographic classes (with respect to age and gender) contribute to the face representations, which in turn, improves the classification and recognition accuracies. Experiments performed on a challenging publicly available database validate the effectiveness of our proposed solution and show its superiority over the existing state-of-the-art methods.