Virtual samples and sparse representation-based classification algorithm for face recognition

Due to the environment and equipment are not controllable, the process of face image acquisition is inevitable to be interfered by external factors, and there are usually only a small number of available face images. Insufficient samples are not conducive to face recognition. Therefore, it is a popular scheme to produce virtual samples based on the available training samples. In this study, the authors first take the symmetry of human face into account, and propose a novel method to generate virtual samples. Then a representation-based classification method and the score fusion strategy are applied to both original face images and virtual images to perform face recognition. Several sparse representation-based classification algorithms are compared on ORL, FERET and GT databases. Experimental results show that the authors' method is effective for improving the face recognition.