Human face feature extraction and classification method

Abstract

The invention relates to a human face feature extraction and classification method. The method includes the following steps that feature dimension reduction is performed on human face images through a 2D-PCA method, and a high-dimensional image matrix is converted into a low-dimensional image matrix; the low-dimensional image matrix is converted into one-dimensional column vectors; according to the one-dimensional vectors of images of a training set, an intra-class divergence matrix S<W> and an inter-class divergence matrix S in the training set are solved, and eigenvalue decomposition is performed on the S<W> and the S respectively, wherein the eigenvalue decomposition formulae are as follows: and ; D<alpha> is used for estimating , D<beta> is used for estimating , and the formulae and can be obtained; the column space W<1> of and the column space W<2> of are respectively solved, and the optimum projection space W=[W<1>,W<2>] of a feature extraction algorithm with two stages of LDA based on 2D-PCA is obtained; the low-dimensional image matrix in the first step is projected into the optimum projection space W, and then feature vectors of the images are obtained; classifier training is performed on the feature vectors obtained in the sixth step through an SVM+NDA model, and a final human face classifier is obtained.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to a face feature extraction and classification method. Background technique [0002] The identification methods of the traditional time attendance system are mainly attendance card and radio frequency card. Due to the separability of the identification person, it is easy to cause the phenomenon of punching the card. Therefore, biometric identification technology has gradually become the main means of identification. At present, the fingerprint time attendance system using biometric identification technology has been widely used. However, the fingerprint attendance system needs special image acquisition equipment to acquire fingerprints, and the image acquisition is touch or contact, which will bring discomfort to users. Moreover, there are many groups or individuals whose fingerprint features are so few that it is difficult to image; when users use fingerprint collection equipment,...

AI Technical Summary

Problems solved by technology

However, the actual face recognition attendance system will also face some challenges. Affected by factors such as illumination, occlusion, scale or movement of the face area, the current face recognition has not yet achieved the expected effect

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