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Face Recognition Using Sparse Fingerprint Classification Algorithm

Face Recognition Using Sparse Fingerprint Classification Algorithm

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Face Recognition Using Sparse Fingerprint Classification Algorithm

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. This paper 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 subjectís 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 binary coefficients vote for their corresponding classes and the maximum-vote class decides the identity of the query image. Experiments were carried out on seven widely-used face databases. 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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  • Model: PROJ8378
  • 999 Units in Stock
  • Manufactured by: ClickMyProjects

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This product was added to our catalog on Monday 25 June, 2018.