Integration method for face recognition by using sparse representation

A technology of face recognition and sparse representation, which is applied in the field of data classification and face recognition, can solve problems such as unstable results and dictionary differences, and achieve the effects of compensating for instability, making it easy to distinguish, and improving the accuracy rate

Inactive Publication Date: 2011-05-25
XIDIAN UNIV
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Problems solved by technology

When there are enough training samples, this method can get good results, but when there are few training samples, the results obtained by this...

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  • Integration method for face recognition by using sparse representation
  • Integration method for face recognition by using sparse representation
  • Integration method for face recognition by using sparse representation

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Embodiment Construction

[0020] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0021] Step 1. Transform all face sample images into a vector, perform normalization and random dimensionality reduction on all vectors, divide the processed vectors into a test sample set and a training sample set at random, and define the test sample set as y , the training sample set is A, where A={A 1 , A 2 , K A N}, N represents the number of categories in the training sample set, A i , i=1, 2, K, N represents the training samples of the i-th class.

[0022] Step 2, use the rotation forest algorithm to generate K rotation matrices, and use the rotation matrix to convert the training sample set A={A 1 , A 2 , K A N} and the test sample set y are mapped to K sets of new training sample sets j=1, 2, K, K and test sample set y j , j=1, 2, K, K.

[0023] Define Y as the label set corresponding to the training sample set A, where Y=[w 1 ,w 2 , K, w N ],w i , i=...

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Abstract

The invention discloses an integration method for face recognition by using sparse representation, which mainly solves the problem of low recognition stability in the conventional K-SVD dictionary learning method. The integration method is realized by the following steps of: generating a rotation matrix by a rotation forest algorithm; randomly projecting the same face sample data to different coordinate systems through the rotation matrix, wherein the projected face sample data is easier to distinguish than the original data; recognizing the projected face sample data by a sparse representation classification method; and voting to select the recognition result of a projected face sample to acquire the recognition result of the original face sample. Compared with the conventional sparse representation-based classification method, the integration method has the characteristics of improving the recognition correctness and the recognition stability, and can be used for a safety verification system.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, relates to data classification, and can be used for face recognition. Background technique [0002] Sparse representation has become a hot spot in the research of digital image processing in recent years. Its idea is to represent the image in the simplest possible way, that is, to capture the important information of the target image with very little data. This idea provides a new way for image representation. Theories and methods have important theoretical significance. [0003] In 2008, A. Yang et al. proposed a face recognition method based on sparse representation. The idea is to regard face recognition as a linear combination process, and the face image of the same person can be used by other people of the person The face image has a good linear representation, that is, the test sample can be linearly represented by the training sample, and the test sample is classified acc...

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Application Information

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IPC IPC(8): G06K9/66
Inventor 王爽焦李成隋国雷杨淑媛侯彪缑水平钟桦霍丽娜高婷
Owner XIDIAN UNIV
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