A gender classification scheme based on multi-region feature extraction and information fusion for unconstrained images

Since gender classification has been interesting in many applications, we proposed a gender classification scheme based on multi-region feature extraction and information fusion in the paper. The proposed gender classification scheme is composed of three parts: pre-processing, multi-region feature extraction, and gender classifier. Before extracting useful information from multiple regions in a facial image, face detection and face orientation correction are performed in the pre-processing. Multi-region feature extraction measures three kinds of features from eyes, internal face, and hair. Since the three kinds of features have their particular properties, a classifier based on decision-level information fusion is utilized to combine these features for gender classification. To evaluate the proposed scheme, a large number of unconstrained images containing different-size faces are captured by using a low-cost webcam and digital cameras. Experimental results show that our proposed scheme can detect facial regions and the location of eyes well. Furthermore, the accuracy of the proposed gender classification scheme is higher than 96 %. These experimental results demonstrate that the proposed scheme can deal with unconstrained images for gender classification.