Cluster-based multi-task Sparse Representation for efficient face recognition

We propose an efficient and accurate classification method based on Sparse Representation based Classification (SRC) for face recognition. In this approach, instead of using all or a subset, we use cluster centers of training samples to build SRC models. Considering the variability and redundancy of training samples, each class will be represented by a different number of representatives. In the next step, different feature vectors are extracted from this abstract training set and different modalities are formed which are then used in a multimodal sparse representation framework to classify unknown test samples. Face recognition experiments on two different face datasets confirm the proposed multimodal method has higher recognition rates in comparison to single-modality methods. The proposed method is also compared to other multi-modality classifiers and results confirm that higher recognition rates can be achieved with this method.