Facial expression recognition: Gabor filters versus higher-order correlators

In this paper we investigate the performance of different feature extraction methods for facial expression recognition based on the higher-order local autocorrelation (HLAC) coefficients and Gabor wavelet filters. We use a Cohn-Kanade database of facial images, organized in training and testing sets, for evaluation. Autocorrelation coefficients are computationally inexpensive, inherently shift-invariant and quite robust against changes in facial expression. The focus is on the difficult problem of recognizing an expression in different resolutions. Results indicate that local autocorrelation coefficients have surprisingly high information content.