Gender Recognition Using 3-D Human Body Shapes

Abstract

Gender recognition has important applications in identity recognition, demographic survey, and human-computer interaction systems. In the past, gender recognition was based on 2-D images or videos, which has many limitations and disadvantages, such as low accuracy and sensitivity to the viewpoint of the camera and lighting conditions. In this paper, we investigate gender recognition using 3-D human body shapes. The 3-D human body shapes used for gender recognition were obtained by laser scanning. Different machine-learning algorithms and feature-extraction methods are investigated and analyzed in this paper. Experimental results show that the support vector machine (SVM) is the best classification algorithm, and the features represented using distributions of normals are very effective for gender recognition. Furthermore, Fourier descriptor (FD) is a robust method to analyze the breast regions and has great potential applications in 3-D human-body-shape-based biometrics. The research demonstrates that our shape-based gender recognition has achieved a very high recognition rate. The techniques provide effective ways for gender recognition and overcome some limitations in 2-D technologies.