Facial expression recognition with dynamic cascaded classifier

In this paper, a new approach for facial expression recognition has been proposed. The approach has imbedded a new feature extraction technique, new multiclass classification approach and a new kernel parameter optimization for support vector machines. The scheme of the approach begins with feature extraction from the input vectors, and the extracted features are transformed into a Gaussian space using compressive sensing techniques. This process ensures feature vector dimensionality reduction and matches the features vectors with radial basis function kernel used in support vector machines for classification. Prior to classification, an optimized parameter for support vector machines training is automatically determined based on an approach proposed which relies on the receiver operating characteristics of the support vector machine classifier. With the optimized kernel parameter, new proposed multiclass classification approach is used to finally classify any vector. In all the experiments conducted on the two facial expression databases with different cross-validation techniques, the proposed approach outperforms its counterparts under the same database and settings. The results further confirmed the validity and advantages of the proposed approach over other approaches currently used in the literature.