Multicore dictionary learning-based color face recognition method

Abstract

The invention discloses a multicore dictionary learning-based color face recognition method. According to the method, a multicore learning technology is applied to the dictionary learning and sparse coding processes of color face data; and through designing a core function selection criterion, optimal core mapping functions are respectively selected for three color components of a color face image training sample set, three characteristic extract coefficient matrixes, structural dictionaries and corresponding sparse codes are respectively learned for the three core mapped color components, the dictionaries obtained through learning are used for carrying out sparse coding on nonlinear characteristics of to-be-recognized samples, and classification and recognition are carried out according to reconstruction errors. The method disclosed in the invention is higher in recognition effect, and has relatively good robustness for the image quality problem.

Description

technical field [0001] The invention specifically relates to a color face recognition method based on multi-core dictionary learning, and belongs to the technical field of face recognition. Background technique [0002] Existing color face recognition methods based on multi-core learning technology include: [0003] For a color face image training sample set X, let n represent the 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 the three color component sample sets of R, G, and B, and d represents the dimension of the color component sample; φ R :R d →H R , φ G :R d →H G , φ B :R d →H B Represents three kernel maps, which respectively transform the three color component samples of R, G, and B from the original d-dimensional linear space R d Mapped to three nonlinear high-dimensional kernel spaces H R 、H G 、H B , H i The dimension of k i Denotes the kernel map φ i The corresponding kernel functi...

AI Technical Summary

Problems solved by technology

[0013] The CD-MK-DA method and the CD-MK-DCA method use three different nonlinear kernel maps for the three color components respectively, but they do not consider how to choose an appropriate kernel map function; and these two methods do not consider how to deal with Image quality problems such as noise interference, partial occlusion and image corrosion, the recognition effect is greatly affected in the color face recognition task with these problems

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