Supervised sparse representation method with a heuristic strategy and face recognition experiments

In this paper we propose a supervised sparse representation method for face recognition. We assume that the test sample could be approximately represented by a sparse linear combination of all the training samples, where the term "sparse" means that in the linear combination most training samples have zero coefficients. We exploit a heuristic strategy to achieve this goal. First, we determine a linear combination of all the training samples that best represents the test sample and delete the training sample whose coefficient has the minimum absolute value. Then a similar procedure is carried out for the remaining training samples and this procedure is repeatedly carried out till the predefined termination condition is satisfied. The finally remaining training samples are used to produce a best representation of the test sample and to classify it. The face recognition experiments show that the proposed method can achieve promising classification accuracy.