Local feature extraction methods for facial expression recognition

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 local binary pattern (LBP) operator. 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 LBP coefficients have surprisingly high information content.