Facial expression recognition based on meta probability codes

Automatic facial expression recognition has made considerable gains in the body of research available due to its vital role in human-computer interaction. So far, research on this problem or problems alike has proposed a wide verity of techniques and algorithms for both information representation and classification. Very recently, Farajzadeh et al. in Int J Pattern Recognit Artif Intell 25(8):1219-1241, (2011) proposed a novel information representation approach that uses machine-learning techniques to derive a set of new informative and descriptive features from the original features. The new features, so called meta probability codes (MPC), have shown a good performance in a wide range of domains. In this paper, we aim to study the performance of the MPC features for the recognition of facial expression via proposing an MPC-based framework. In the proposed framework any feature extractor and classifier can be incorporated using the meta-feature generation mechanism. In the experimental studies, we use four state-of-the-art information representation techniques; local binary pattern, Gabor-wavelet, Zernike moment and facial fiducial point, as the original feature extractors; and four multiclass classifiers, support vector machine, k-nearest neighbor, radial basis function neural network, and sparse representation-based classifier. The results of the extensive experiments conducted on three facial expression datasets, Cohn-Kanade, JAFFE, and TFEID, show that the MPC features promote the performance of facial expression recognition inherently.