GA-Driven LDA in KPCA space for facial expression recognition

Automatic facial expression recognition has been studied comprehensively recently, but most existent algorithms for this task perform not well in presence of nonlinear information in facial images. For this sake, we employ KPCA to map the original facial data to a lower dimensional space. Then LDA is applied in that space and we derive the most discriminant vectors using GA. This method has no singularity problem, which often arises in the traditional eigen decomposition-based solutions to LDA. Other work of this paper includes proposing a rather simple but effective preprocessing method and using Mahalanobis distance rather than Euclidean distance as the metric of the nearest neighbor classifier. Experiments on the JAFFE database show promising results.