Applying weighted smoothed norm in sparse representation classification for face recognition

Classification and recognition is one of the most important methods of extracting information from images, and among them, facial image recognition as one of the most efficient biometric features for human identification has always been of interest, and extensive research has been conducted in this field in recent years. So far, various solutions for face recognition have been proposed by researchers, but among them, the use of Sparse representation classification has been considered as an effective and specific solution. One of the features of Sparse representation is to obtain features from input images without the need of feature extraction methods, therefore, in this article, the proposed method is aimed at applying weighted smoothed ℓ0 norm for face recognition using Sparse representation.To check the performance of the proposed method, ORL and AR databases including images of different facial expressions have been used, and the simulated results show that the method performs very well compared to other well-known methods in the field of face recognition.