In this paper, we show how to extract gender discriminating features from 2.5D facial needle-maps. The standard eigenspace analysis method for non-Euclidean data is principal geodesic analysis (PGA). Based on PGA, we propose a novel supervised weighted PGA method which incorporates local weights into standard PGA to improve gender discriminating capability of the extracted features. The weight map is iteratively optimized from the labeled data, which is different from other gender relevant weights used in the literature [1]. Experimental results illustrate the effectiveness of this method and its successful application to gender classification.
['Jing Wu', 'William A. P. Smith', 'Edwin R. Hancock', 'Michal Kawulok']
Extracting gender discriminating features from facial needle-maps