Color face recognition method based on semi-supervised multi-view dictionary learning

Abstract

The invention discloses a color face recognition method based on semi-supervised multi-view dictionary learning. The method applies multi-view learning technology to dictionary learning of semi-supervised color face images. In the training phase, the method learns the structured dictionaries of each color component separately and makes these dictionaries orthogonal to each other to remove the correlation between each color component, making full use of the complementary color difference information between each color component; and the method In the dictionary learning process, color face image samples without class labels are used to participate in the training, and the information of all training samples is fully utilized. In the classification test stage, the method accumulates the reconstruction errors of each color component and uses the dictionary corresponding to each type of training samples to reconstruct the test samples, and finally classifies the test samples into the class with the smallest cumulative reconstruction error. The recognition effect of the invention is higher, and the color face recognition ability is obviously enhanced through the semi-supervised multi-view dictionary learning.

Description

technical field [0001] The invention specifically relates to a color face recognition method based on semi-supervised multi-view dictionary learning, and belongs to the technical field of face recognition. Background technique [0002] (1) K singular value decomposition 2 color extension 2 method with consistent labels (CE2-LC-KSVD2, Shi Jinglan, Chang Kan, Zhang Zhiyong, Qin Tuanfa, "Dictionary Learning Algorithm for Color Image Face Recognition", Telecommunications Technology, 56( 4): 365-371, [0003] 2016): [0004] For a color face image training sample set X, let n represent the number of all color face image training samples, c represent the category number of all color face image training samples, X R ∈R d×n 、X G ∈R d×n 、X B ∈R d×n Respectively represent three color component sample sets of R, G and B, and d represents the dimension of color component samples. The objective function of CE2-LC-KSVD2 method is [0005] [0006] Among them, X'=(I+γ / d·E)[X R...

AI Technical Summary

Problems solved by technology

[0008] Although the CE2-LC-KSVD2 method utilizes the correlation between the color channels by modifying the inner product calculation criterion of the orthogonal matching pursuit algorithm in the sparse coding stage, this modification only forces the selected dictionary atoms to consider the average color and does not make full use of it. Color information for color images

In addition, the CE2-LC-KSVD2 method is a supervised dictionary learning method, which can only use class-labeled color face image samples in the training stage, and cannot make full use of the large number of non-class-labeled color face image samples

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