Gender Classification Using Single Frontal Image Per Person: Combination of Appearance and Geometric Based Features

Today, many social interactions and services depend on gender. In this paper, we introduce a single image gender classification algorithm using combination of appearance-based and geometric-based features. These include Discrete Cosine Transform (DCT), and Local Binary Pattern (LBP), and geometrical distance feature (GDF). The novel feature, GDF proposed in this paper, is inspired from physiological differences between male and female faces. Combination of appearance-based features (DCT and LBP) with geometric-based feature (GDF) leads to higher gender classification accuracy. Our system estimates gender of the input image based on the majority rule. If the results of DCT and LBP features are not identical, gender classification will be based on GDF feature. The proposed method was evaluated on two databases: AR and ethnic. Experimental results show that the novel geometric feature improves the gender classification accuracy by 13%.