Sparse coding-based human face identification method

A face recognition and sparse coding technology, which is applied in the field of face recognition based on sparse coding, can solve problems such as difficulty in determining the true distribution of residuals, occlusion or noise interference, and poor quality of face images, so as to reduce computational complexity , reduce the vector dimension, improve the recognition rate and the effect of robustness

Active Publication Date: 2017-06-13
SUN YAT SEN UNIV
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AI Technical Summary

Problems solved by technology

When the face is occluded or any pixel is corrupted, the true distribution of the residual is difficult to determine
[0008] Although many face recognition methods have been proposed, due to the complexity of the face itself and the environment, face recognition technology still has many unsolved difficulties, especially when the face image quality is poor, occlusion or noise interference Time

Method used

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  • Sparse coding-based human face identification method
  • Sparse coding-based human face identification method
  • Sparse coding-based human face identification method

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Experimental program
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Embodiment 1

[0043] Such as figure 1 As shown, the method provided by the invention specifically includes the following steps:

[0044] The first step, assuming that there are k known objects in the training sample set, among which n objects contained in the i-th object i training samples are expressed as a matrix where i=1,2,...,k,v ij ∈R m ,j=1,2,...,n i , v ij Represents the column vector corresponding to the j-th training sample of the i-th object, m represents v ij dimension, the training sample set A can be expressed as:

[0045]

[0046] in Indicates the total number of training samples;

[0047] In the second step, let the test sample be expressed as y, and let the reconstructed sample y rec Initialized to the mean of all training samples;

[0048] The third step, calculate y and y rec The residual between e=y-y rec ;

[0049] The fourth step is to define the diagonal matrix P=diag(p 1 ,p 2 ,...,p m );

[0050] in s.t.e=y-y rec , e d Represents the dth compo...

Embodiment 2

[0063] In order to prove the effectiveness and robustness of the method provided by the present invention, two groups of comparative experiments were carried out in this implementation. In this embodiment, the first set of comparative experiments is done on the Extended Yale-B face database. In the first set of experiments, Subset1 and Subset2 in the EYB face database were used as training sets, and there were a total of 717 training sample images, which were collected under suitable lighting conditions. Using Subset3 as the test set, there are a total of 453 test samples, and these images are all subject to strong lighting. The size of each image is 96×84.

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Abstract

The invention relates to a sparse coding-based human face identification method. In a conventional sparse coding method, residues in coding are often assumed to meet a certain probability distribution form such as Laplace distribution or Gaussian distribution in advance, and based on this, an l1 or l2 norm form is proposed for solving a sparse coding coefficient; and due to the abovementioned processing means, the robustness of human face identification is seriously influenced, especially when shielding, noises or interferences in other forms exist. The method provided by the invention introduces an iterative optimization thought for the deficiencies of the conventional sparse coding method, and mainly solves the following two problems: firstly, the distribution form of residual errors does not need to be assumed in advance, so that the influence of an unreasonable residual error distribution function on the robustness of the human face identification is avoided; and secondly, a part of available pixel points are selectively reserved to perform identification, so that the problems of shielding, pixel damage and the like are better solved while the calculation amount is greatly reduced, and robuster identification performance is obtained.

Description

technical field [0001] The present invention relates to the field of face recognition, and more specifically, to a face recognition method based on sparse coding. Background technique [0002] With the rapid development of bioengineering technology and computer technology, people's demand for security identification and identity verification issues is getting higher and higher. Face recognition technology with the advantages of non-contact, non-intrusive, friendliness, and scalability stands out among a variety of biometric technologies, and has been widely used in video retrieval, public security monitoring, access control, identity recognition and other fields. With the further maturity of technology and the improvement of social recognition, face recognition technology will be applied in more fields. [0003] At present, the commonly used face recognition methods can be roughly divided into three types: face recognition methods based on 2D images, face recognition method...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
CPCG06V40/161G06V40/168G06V10/40G06V10/513G06F18/214
Inventor 郑慧诚连丽娜董佳羽
Owner SUN YAT SEN UNIV
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