Head-shoulder based gender recognition

This paper proposes a novel gender recognition method based on the head-shoulder part of human body. The head-shoulder area contains much information that could be cues to infer the gender of a person, such as hair-style, face, neckline style and so on. A rich high-dimensional feature descriptor is designed to extract gradient, texture and orientation information from the head-shoulder area, then Partial Least Squares (PLS) is employed to learn a very low dimensional discriminative subspace. Features are projected into the low dimensional subspace and linear SVM is employed to learn an efficient classification model between the male and female categories. Experimental results on a large real-world dataset demonstrate the effectiveness of the proposed method. © 2013 IEEE.