Face Recognition Method Based on Structural Factor Analysis

Abstract

The invention discloses a face recognition method based on structuralized factor analysis. The problem that the face recognition accuracy rate is low due to the fact that local clustering features and a global distribution structure of face image data cannot be kept in the prior art is mainly solved. The face recognition method comprises the achieving steps of 1 dividing a face image data set; 2 performing clustering analysis on all training sample data; 3 calculating an optimal feature projection matrix through Gibbs sampling; 4 extracting low-dimension features of all test sample data and the training sample data; 5 recognizing a face image. The face recognition method synthesizes the local clustering features and the global distribution structure of the face image data and improves the face recognition accuracy rate.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a face recognition method based on structural factor analysis in the technical field of pattern recognition and machine learning. The invention can be applied to identity recognition and information security, and improves the accuracy of face recognition by extracting face features with fewer dimensions. Background technique [0002] Face recognition technology is a computer technology that uses computers to analyze face images and extract effective visual feature information for identity identification. Among the existing biometric identification technologies, face recognition technology has been widely used due to its advantages of simple operation and easy implementation. The dimensionality of face images is usually high, and there are strong similarities between different face images. If only the original face images are used for identity identification, the f...

AI Technical Summary

Problems solved by technology

The disadvantage of this method is that in the feature extraction process, only non-negativity constraints are imposed on the feature projection matrix and the low-dimensional features of the training image, and the local clustering characteristics and global distribution structure of the face image in the high-dimensional feature space are ignored. , resulting in relatively low accuracy of face recognition

The disadvantage of this method is that in the process of feature extraction, the local preservation non-negative matrix factorization method only considers the local distribution structure of the face image in the high-dimensional feature space, ignoring its global distribution structure

The disadvantage of this method is that the addition of new sample data will destroy the original local neighbor structure, resulting in poor stability of the features extracted from the new sample data, which limits the practical application of this method

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