Face recognition method based on multi-view collaborative complete discriminant subspace learning
A subspace learning and face recognition technology, applied in the field of face recognition based on multi-view collaborative complete discriminative subspace learning, can solve the problem of enhancing the discriminativeness of the complete subspace, the adverse effects of multi-view subspace learning, and the inconsistency of single view information. Integrity and other issues, to achieve the effect of enhancing the identification performance and improving the identification performance
Active Publication Date: 2018-09-28
江西前进系统工程有限公司
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[0008] The purpose of the present invention is to provide a face recognition method based on multi-view cooperative complete discriminant subspace learning, which solves the adverse effects of incompleteness of single view information and noise in multi-view on multi-view subspace learning and enhances Latent complete subspace discriminative problem
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[0019] A face recognition method based on multi-view collaborative complete discriminative subspace learning, such as figure 1 Shown, assuming D={z vij |1≤v≤m,1≤i≤c,1≤j≤n i} represents the feature representation of n training samples in m views, X={x ij |1≤i≤c,1≤j≤n i} represents the complete feature representation of these n training samples in the potential complete subspace, where m and c represent the number of views and the number of categories of samples, respectively, n i Indicates the number of samples of class i, The method includes the following steps:
[0020] (1) Use the objective function based on Cauchy loss and Fisher discriminant analysis to obtain the complete feature representation of n training samples in the potential complete subspace X, v view generation functions and v non-negative collaborative learning weights Π=[π 1 ,...,π v ];
[0021] (2) In view of the non-convex nature of the objective function, the solutions X, W and Π of the objec...
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A face recognition method based on multi-view collaborative complete discriminant subspace learning is provided. The method comprises the following steps: (1) using an objective function based on Cauchy loss and Fisher discriminant analysis to obtain complete feature representation as shown in the specification of the number as shown in the specification of training samples in a potential completesubspace, the number as shown in the specification of view generation functions as shown in the specification, and the number as shown in the specification of non-negative collaborative learning weights as shown in the specification; (2) given the non-convex nature of the objective function, obtaining two solutions as shown in the specification of the objective function by using the alternate solution method; (3) based on the solved view generation functions as shown in the specification and the non-negative collaborative learning weights as shown in the specification, solving complete feature representation of test samples in the complete discriminant subspace; and (4) based on the Euclidean distance between the test sample and the training sample in the complete discriminant subspace, classifying the test samples by using a nearest neighbor classifier. Compared with the existing multi-view face recognition method, the method provided by the present invention can more effectively fuse multi-view information and mine discriminant information, and is an effective multi-view face recognition method.
Description
technical field [0001] The invention relates to a face recognition method based on multi-view collaborative complete discrimination subspace learning. Background technique [0002] In practical applications, data can usually be represented by multiple views. For example, in multimedia content understanding, a piece of multimedia content can be described by video signals and audio signals; in photo sharing websites, an image can be indexed by different visual features or by user-set tags. Usually, the information of different views is used to describe the characteristics of different aspects of an object, and the information of different views is often complementary. Therefore, it is often not possible to fully describe an object using information obtained from only one view. In applications such as classification, clustering, and retrieval, the use of connection and difference information between views to improve performance has received more and more attention, and these ...
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IPC IPC(8): G06K9/62G06K9/00
CPCG06V40/16G06F18/21322G06F18/21324G06F18/24143G06F18/214
Inventor 董西伟杨茂保王玉伟周军董小刚李立现邓安远邓长寿
Owner 江西前进系统工程有限公司
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