Facial expression recognition using a combination of multiple facial features and support vector machine

This paper presents a novel facial expression recognition (FER) technique based on support vector machine (SVM) for the FER. Here it is called the FERS technique. First, the FERS technique develops a face detection method that combines the Haar-like features method with the self-quotient image (SQI) filter. As a result, the FERS technique possesses better detection rate because the face detection method gets more accurate in locating face regions of an image. The main reason is that the SQI filter can overcome the insufficient light and shade light. Subsequently, three schemes, the angular radial transform (ART), the discrete cosine transform (DCT) and the Gabor filter (GF), are simultaneously employed in the design of the feature extraction for facial expression in the FERS technique. More specifically, they are employed in constructing a set of training patterns for the training of an SVM. The FERS technique then exploits the trained SVM to recognize the facial expression for a query face image. Finally, experimental results show that the recognition performance of the FERS technique can be better than that of other existing methods under consideration in the paper.