Gender recognition with limited feature points from 3-D human body shapes
In this paper, we investigate the possibility of using limited feature points (shape landmarks) from 3-D human body shapes to recognize the gender of human beings. Several machine learning algorithms and feature extraction algorithms (principal component analysis and linear discriminant analysis) are investigated and analyzed in this paper. Experimental results on a large dataset containing 2484 3-D shape models show that limited feature points (shape landmarks) can be used for gender recognition and can achieve high recognition rate, which provides a fast gender recognition technique. The research provides a potential research direction for gender recognition.