Optimal metric selection for improved multi-pose face recognition with group information

We address the limitation of sparse representation based classification with group information for multi-pose face recognition. First, we observe that the key issue of such classification problem lies in the choice of the metric norm of the residual vectors, which represent the fitness of each class. Then we point out that limitation of the current sparse representation classification algorithms is the wrong choice of the ℓ₂ norm, which does not match with data statistics as these residual values may be considerably non-Gaussian. We propose an explicit but effective solution using ℓ_p norm and explain theoretically and numerically why such metric norm would be able to suppress outliers and thus can significantly improve classification performance comparable to the state-of-arts algorithms on some challenging datasets.