Low-rank partitioning sparse representation human face identifying method
A sparse representation and face recognition technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problem that face recognition methods cannot effectively deal with face image occlusion, camouflage and illumination changes at the same time
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[0076] The technical scheme of the present invention will be further described in detail below in conjunction with the accompanying drawings:
[0077] First select the database you want to experiment, such as AR face database. The AR database contains 126 subjects and a total of 4000 face images. In the experiment, we select 50 subjects from male pictures, and randomly select 20 from each subject as training pictures to form the training matrix, and the other 6 as test pictures to form the test matrix.
[0078] Perform low-rank matrix decomposition on the training matrix, and apply the new low-rank algorithm proposed in the present invention to improve the incoherence between classes in the matrix.
[0079] The final objective function of the algorithm is expressed as:
[0080] min A i , E i | | A i | | * + λ 1 | | E i | | 1 + λ 2 | | A i - M i | | F 2 s . t . ...
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