A Method of Incremental Learning Face Recognition Based on Local Preserving Nonnegative Matrix Factorization

Abstract

The invention discloses an incremental learning and face recognition method based on locality preserving nonnegative matrix factorization (LPNMF), and relates to the technical field of pattern recognition. The incremental learning and face recognition method is essentially a face recognition method based on on-line learning. The method comprises the step (a) of face image preprocessing, the step (b) of initial sample training, the step (c) of incremental learning and the step (d) of face recognition. The incremental learning and face recognition method can be applied to a linear face recognition system and a non-linear face recognition system, keeps local structures of original space of face images, greatly improves recognition rate, and can be actually applied in an on-line mode.

Description

technical field [0001] The invention relates to the technical field of pattern recognition, in particular to an incremental learning face recognition method based on Locally Preserving Non-Negative Matrix Factorization (LPNMF). Background technique [0002] In the past two decades, face recognition technology, as an efficient biometric identification technology, has been increasingly valued by academia and industry. The key to face recognition research is feature extraction, which can be based on global features or local features. [0003] The eigenface method, also known as Principal Component Analysis (PCA), is a face recognition method based on global feature extraction. PCA extracts the principal components, reduces redundant information between samples, and achieves the purpose of dimensionality reduction. However, the eigenface method has several disadvantages: a) how to select the pivot is still a problem; b) when the training samples are linearly inseparable, the r...

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Problems solved by technology

However, the eigenface method has several disadvantages: a) how to select the pivot is still a problem; b) when the training samples are linearly inseparable, the recognition rate of the eigenface method is very low; c) PCA based on global features cannot extract local features

[0005] At present, most of the algorithms based on NMF have two shortcomings, that is, the decomposition speed is slow and the increase of training samples or categories must be repeated learning, which greatly limits the application of NMF in practice.

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