Face Recognition Using Sparse Fingerprint Classification Algorithm
Abstract– Unconstrained face recognition is still an open problem as state-of-the-art algorithms have not yet reached high recognition performance in real-world environments. Addresses this problem by proposing a new approach called Sparse Fingerprint Classification Algorithm (SFCA). In the training phase, for each enrolled subject, a grid of patches is extracted from each subject’s face images in order to construct representative dictionaries. In the testing phase, a grid is extracted from the query image and every patch is transformed into a binary sparse representation using the dictionary, creating a fingerprint of the face. The results demonstrate that when the size of the dataset is small or medium ( e.g., the number of subjects is not greater than one hundred), SFCA is able to deal with a larger degree of variability in ambient lighting, pose, expression, occlusion, face size, and distance from the camera than other current state-of-the-art algorithms.
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