Integrating independent components and linear discriminant analysis for gender classification

Computer vision and pattern recognition systems play an important role in our lives by means of automated face detection, face and gesture recognition, and estimation of gender and age. We have developed a gender classifier with performance superior to existing gender classifiers. This paper addresses the problem of gender classification using frontal facial images. The testbed consists of 500 images (250 females and 250 males) randomly withdrawn from the FERET facial database. Independent component analysis (ICA) is used to represent each image as a feature vector in a low dimensional subspace. A classifier based on linear discriminant analysis (LDA) is used in this lower dimensional subspace. Our experimental results show a significant improvement in gender classification accuracy and we obtain an accuracy of 99.3%.