Incremental learning and face recognition method based on locality preserving nonnegative matrix factorization ( LPNMF)
A non-negative matrix decomposition, face recognition technology, applied in the field of incremental learning face recognition, can solve the problems of low recognition rate of eigenface method, inability to extract local features, slow decomposition speed, etc.
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[0039] The present invention will be further described below. Referring to the attached picture:
[0040] A method of incremental learning face recognition based on locally preserving non-negative matrix factorization, comprising the following steps:
[0041] a) Preprocessing of face images: normalize each face image into a sample of the same specification;
[0042] b) Initial sample training: Use the LPNMF algorithm to calculate the base matrix W and coefficient matrix H of the initial sample:
[0043] 1) Put each sample v i Constructed into an initial sample training matrix V=[v 1 ,v 2 ,...,v n ];
[0044] 2) Set the maximum number of iterations t, iteratively update W and H mutually, so that WH≈V;
[0045] 3) In step 2, W and H are updated according to the following rules:
[0046] W ia ← W ia ( V H ...
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