Combining features and decisions for face detection

We propose a novel face detection algorithm which detects faces in color images using a combination of feature and decision fusion mechanisms. In addition to commonly used skin color information, two additional features, namely average face template matching score and horizontal edge template matching score are utilized. A mean shift algorithm operating on the combined feature space is used to determine face candidate areas. The face candidate and its flipped pattern are then inputted to a multiple layer perceptron based classifier. Two outputs along with the correlation value between the candidate and its flipped pattern are then combined to give the final decision. Experimentation on two different test databases indicates that the proposed method performs well under a variety of scale, expression and environmental conditions, outperforming commonly used approaches.