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Facial expression recognition using Fisher weight maps

In recent years, much research has been done on face image analysis. There are two major approaches: local-feature-based and image-vector-based. We propose a hybrid of these two approaches. Our method uses higher-order local auto-correlation (HLAC) features and Fisher weight maps. HLAC features are computed at each pixel in an image. These features are integrated with a weight map to obtain a feature vector. The optimal weight map, called a Fisher weight map, is found by maximizing the Fisher criterion of feature vectors. Fisher discriminant analysis is used to recognize an image from the feature vector. Our experiments on facial expression recognition demonstrate the effectiveness of Fisher weight maps for objectively quantifying the importance of each facial area for classification of expressions.