Weighted average integration of sparse representation and collaborative representation for robust face recognition

Sparse representation is a significant method to perform image classification for face recognition. Sparsity of the image representation is the key factor for robust image classification. As an improvement to sparse representation-based classification, collaborative representation is a newer method for robust image classification. Training samples of all classes collaboratively contribute together to represent one single test sample. The ways of representing a test sample in sparse representation and collaborative representation are very different, so we propose a novel method to integrate both sparse and collaborative representations to provide improved results for robust face recognition. The method first computes a weighted average of the representation coefficients obtained from two conventional algorithms, and then uses it for classification. Experiments on several benchmark face databases show that our algorithm outperforms both sparse and collaborative representation-based classification algorithms, providing at least a 10% improvement in recognition accuracy.