Face recognition method based on multi-kernel discriminative color space
A color space and color face technology, applied in the field of face recognition, can solve the problems that it is difficult to fully adapt to the nonlinear characteristics of face images, and it is difficult to guarantee the recognition effect
Active Publication Date: 2019-08-23
中通服网盈科技有限公司
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[0009] The DCS method is based on linear discriminant analysis technology, and it is difficult to fully adapt to the complex nonlinear characteristics of face images (for example, illumination changes, expression changes, posture changes, etc.), so it is difficult to guarantee the recognition effect
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[0050] Below in conjunction with the accompanying drawings and specific embodiments, the technical solutions of the present invention are described in further detail:
[0051] figure 1 This is a flow chart of the face recognition method based on multi-core discrimination color space of the present invention, and its specific content will not be repeated here.
[0052] The experimental verification uses the Face Recognition Grand Challenge (FRGC) version 2Experiment4 color face database (P.J.Phillips, P.J.Flynn, T.Scruggs, K.Bowyer, J.Chang, K.Hoffman, J.Marques, J.Min, and W. Worek. Overview of the Face Recognition Grand Challenge. IEEE Conf. Computer Vision and Pattern Recognition, vol. 1, pp. 947-954, 2005.). The database is large in scale and includes three sub-databases: training, target, and query. The training sub-database contains 12,776 pictures of 222 people, the target sub-database contains 16,028 pictures of 466 people, and the query sub-database contains 8,014 pic...
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The invention discloses a face recognition method based on multi-kernel discrimination color space, which applies multi-kernel learning technology to color face images, uses three different nonlinear kernel mappings for the three color components of the color face image, and then Color space transformation is performed by multi-kernel discriminative color space method. In the new multi-kernel discriminative color space, the enhanced Fisher linear discriminant model method is used to extract features for each color component, and the feature vectors of the three color components of each color face image are concatenated into a column vector, and then the nearest Neighbor classifier for classification and identification. The identification effect of the invention is higher, and after the color human face image is transformed into a multi-core identification color space, the classification ability of identification features is obviously enhanced.
Description
technical field [0001] The invention relates to a face recognition method based on multi-core discrimination color space, and belongs to the technical field of face recognition. Background technique [0002] Existing face recognition methods for discriminating color spaces (DCS) (C. Liu, "Learning the uncorrelated, independent, and discriminating color spaces for face recognition", IEEE Trans. Information Forensics and Security, vol. 3, no. 2, pp .213-222, 2008.) through a linear transformation W ∈ R 3×3 Transform a color image from RGB color space to discriminative color space: [0003] [0004] Among them, R(x,y), G(x,y) and B(x,y) respectively represent the pixel values of R, G and B of a pixel point (x,y) in a color image, D 1 (x,y), D 2 (x,y) and D 3 (x, y) represent the pixel values of the three new color components in the discriminative color space, respectively. The color space transformation matrix W is solved as follows: [0005] [0006] Among them...
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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V40/168G06V40/172G06V10/56G06F18/24147
Inventor 刘茜陈曦陈书圆张赟荆晓远
Owner 中通服网盈科技有限公司