Face recognition method based on L2 regularization gradient constraint sparse representation
A sparse representation and face recognition technology, applied in the field of image recognition, can solve problems such as low recognition accuracy and insufficient use of images, and achieve the effect of improving recognition accuracy
Active Publication Date: 2019-11-15
NANJING UNIV OF POSTS & TELECOMM
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However, these methods only use the features of the visual level, and do not make full use of other information of the image. In addition to the features of the visual level, gradient information is also an important feature of image processing and recognition.
[0004] However, the existing face recognition methods using sparse representation have the problem of low recognition accuracy
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[0037] The following will clearly and completely describe the technical solutions in the embodiments of the application with reference to the drawings in the embodiments of the application. Apparently, the described embodiments are only some of the embodiments of the application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application. The relevant directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship between the components in a certain posture (as shown in the drawings). Sports conditions, etc., if the specific posture changes, the directional indication will also change accordingly.
[0038] As mentioned in the background art, the existing face recognition method using sparse representatio...
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The invention discloses a face recognition method based on L2 regularization gradient constraint sparse representation. The method comprises: acquirnig a training sample set; calculating a representation coefficient of a to-be-recognized sample on a training sample of the training sample set based on the face image gradient recovery constraint information and an L2 regularization sparse representation method; calculating a residual error of the to-be-identified sample on each type of training sample of the training sample set by adopting the representation coefficient of the to-be-identified sample on the training sample; and outputting the training sample category corresponding to the calculated minimum residual error as the category of the to-be-identified sample. According to the scheme, the accuracy of face recognition can be improved.
Description
technical field [0001] The invention belongs to the technical field of image recognition, in particular to a face recognition method based on L2 regularization gradient constrained sparse representation. Background technique [0002] Face recognition is a popular research topic in the field of computer vision. It integrates computer image processing technology and statistical technology, and is widely used in various fields due to its non-contact and non-intrusive advantages, such as: Financial field, public security system, social security field, face recognition at airport border inspection, etc. [0003] Sparse representation has achieved remarkable performance in face recognition. Sparse representation methods usually add some constraints. The most widely used constraints include L1 regularization, L2 regularization, and L21 regularization. Among them, the representation method based on L2 regularization has an obvious advantage that it has a closed-form expression solu...
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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/16G06V40/172G06F18/24G06F18/214
Inventor 张皖高广谓朱冬汪焰南吴松松岳东
Owner NANJING UNIV OF POSTS & TELECOMM