Weighted voting of sparse representation classifiers for facial expression recognition

We present a new algorithm for facial expression recognition that is robust to occlusion. The facial image is divided into equal sized regions, and a Sparse Representation Classifier (SRC) classifies the facial expression in each region. These classification decisions must be combined and different voting methods were considered. A weighted voting method where the vote assigned to each class in a region was based on the class representation error led to the best recognition results under a variety of occlusion conditions. The recognition rate of our algorithm remains very high for un-occluded images (95.3% success). With large occluded regions (≥25% of the image), it significantly outperforms an SRC algorithm based on the entire image and a Gabor-based algorithm. Since each subimage problem can be solved independently before combining decisions, processing can be done in parallel leading to a fast SRC based classification decision if implemented on a multi-core system.