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

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

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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.

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