Gender identification using frontal facial images

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. This paper addresses the problem of gender classification using frontal facial images. We have developed gender classifiers with performance superior to existing gender classifiers. We experiment on 500 images (250 females and 250 males) randomly withdrawn from the FERET facial database.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. Different classifiers are studied in this lower dimensional space. Our experimental results show the superior performance of our approach to the existing gender classifiers. We get a 96% accuracy using support vector machine (SVM) in ICA space.