AGE AND GENDER RECOGNITION USING INFORMATIVE FEATURES OF VARIOUS TYPES

Gender recognition and age classification are important ap-plications of face analysis. The vast majority of the existing solutions focus on a single visual descriptor which often en-codes 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 there-fore 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 demo-graphic classes (with respect to age and gender) contribute to the face representations, which in turn, improves the classi-fication and recognition accuracies. Experiments performed on a challenging publicly available database validate the ef-fectiveness of our proposed solution and show its superiority over the existing state-of-the-art methods. Index Terms — gender recognition, age classification, uniform LBP, face processing, color histogram, feature selec-tion 1