Human face recognition method based on sparse representation
A technology of face recognition and sparse representation, which is applied in the field of face recognition based on sparse representation, can solve the problems of low recognition efficiency, low recognition accuracy, and inability to adapt to non-rigid visual changes of facial images, achieving high accuracy and efficiency, High recognition accuracy and calculation efficiency, the effect of improving recognition accuracy and calculation efficiency
Active Publication Date: 2018-11-06
NANJING UNIV OF POSTS & TELECOMM
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For practical applications, Wagner et al. proposed an improved face recognition system. Although it is not constrained by the linear correlation of test samples, it still has disadvantages such as low recognition accuracy, low recognition efficiency, and inability to adapt to non-rigid visual changes in facial images.
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[0037] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.
[0038] Such as figure 1 Shown, described a kind of face recognition method based on sparse representation, comprises the following steps:
[0039] Step 1: first input the training sample matrix, concatenate all the training samples in the training sample matrix with all object categories, and then input a test sample;
[0040] Among them, the training sample matrix is A=[X 1,1 ,X 1，2 ,···,X K,N ], the test sample is represented by Y∈Rm;
[0041] Among them, for the training samples, A i =[X i，1 , X i，2 ,···,X i，ni ]∈R m Represents the set of training samples in the i-th layer, where X i，j ∈R m Represents the vector of all pixels m in the facial image I; the goal of face recognition is to test any test image Y∈R m Identify the class it is in; at the beginning we do not know all the members i of the test image Y, so we define a new ...
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The invention discloses a human face recognition method based on sparse representation. The human face recognition method comprises the following steps of 1, inputting a training sample matrix firstly, cascading all training samples and all object classes, and then inputting a test sample; 2, calibrating each training sample in the training sample matrix and the test sample to obtain a calibratedtraining sample matrix; 3, standardizing the calibrated training sample matrix; 4, calculating an error between each training sample in the standardized calibrated training sample matrix and a corresponding standard graph to obtain an error value; 5, calculating a residual error between the test sample and the corresponding standard graph; 6, setting identities for all the object classes; and 7, outputting the object class corresponding to the training sample corresponding to the minimum error value to serve as the object class of the test sample. The method has the advantages that the non-rigid visual change of a face image can be better adapted, and the recognition precision and the calculation efficiency are high.
Description
technical field [0001] The invention relates to the technical field of image data processing, in particular to a face recognition method based on sparse representation. Background technique [0002] With the rapid development of big data technology, more and more facial images have been uploaded to the Internet. Face recognition, as the most important vision task, recognizes specific identities from unknown objects with facial image features, has been extensively studied in computer vision, such as facial emotion recognition, video surveillance, and biometrics, etc. [0003] Recently, Sparse Representation Model (SRM) was proposed for the task of face recognition. The main idea is to reconstruct the test samples on a full dictionary, the basic element being the training face images themselves. Once a test image can be represented linearly across space by all training samples, sparse reconstruction can be used to identify relevant classes. SRM achieves impressive results a...
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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/172G06F18/22G06F18/214
Inventor 周全从德春杨文斌卢竞男王雨
Owner NANJING UNIV OF POSTS & TELECOMM