Method for identifying human face based on LDA subspace learning

Abstract

The invention discloses a method for identifying a human face based on local data area (LDA) subspace learning. The method is characterized by comprising the following steps of: performing pre-processing, namely rotating uprightly a human face image; calculating a gradient multi-scale local phase quantification (GMLPQ) characteristic set of the human face image; screening out a candidate characteristic subset in the GMLPQ characteristic set by using an Adaboost selector; analyzing to acquire a human face characteristic template by using an LDA subspace analyzer; and matching the human face characteristic template with a pre-built human face characteristic template base so as to acquire identity information of a person to be identified. In the method, an optimized central point acquired by learning is used as a central point of a cosine distance, so the effectiveness of metering the sample similarity by calculating a sample distance is improved, and the classification performance of the subspace method is enhanced. The method is high in environmental suitability, and identification rate and error identification rate under vague images, low resolution and various illumination conditions, and quick in calculation speed, particularly suitable for embedded products, and can be popularized and used on a large scale.

Description

technical field [0001] The invention belongs to a face recognition method. Background technique [0002] Face recognition technology is one of the biometric technologies that are currently being vigorously developed. The face recognition system mainly includes data acquisition subsystem, face detection subsystem and face recognition subsystem. Face feature extraction is the most critical technology of the face recognition subsystem. A good face feature extraction technology will make the extracted face feature value smaller and better in discrimination performance, which can improve the recognition rate and reduce the false recognition rate. The existing face feature extraction methods mainly include: based on geometric features, based on subspace analysis, based on wavelet theory, based on neural network, based on hidden Markov model, based on support vector machine and based on 3D model. method. The idea of ​​the method based on geometric features is to extract the rela...

AI Technical Summary

Problems solved by technology

In the existing methods, it is assumed that the center point is located at the origin during the test phase, and the optimal selection of the center point is ignored.

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