Single-training sample face recognition method based on blocking consistency LBP (Local Binary Pattern) and sparse coding
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A face recognition and sparse coding technology, applied in the field of face recognition, can solve the problems of easy confusion, algorithm loss of original information, poor recognition rate and robustness, etc., to avoid errors, high recognition rate and robustness. Effect
Inactive Publication Date: 2012-11-28
SHANGHAI JILIAN NETWORK TECH CO LTD
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[0018] The purpose of the present invention is to provide a face recognition method based on block LBP and sparse coding in order to solve th...
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[0043] The specific implementation of the face recognition method based on block LBP and sparse coding of the present invention will be explained below in conjunction with the accompanying drawings, but it should be noted that the implementation of the present invention is not limited to the following embodiments.
[0044] A face recognition method based on block LBP and sparse coding. Firstly, the face image is divided into blocks to count the LBP histogram, and then the consistent LBP histogram is counted to obtain the feature vector corresponding to the entire image, and then the face is produced. The image training set matrix represents the test image as a linear combination on the training set, and finally solves the sparsest solution of the linear combination coefficient vector x.
[0045] Concrete operational steps of the inventive method are as attached figure 1 shown.
[0046] 1. Block statistics LBP histogram
[0047] First, the face image is divided into grids ac...
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Abstract
The invention belongs to the technical field of digital image processing and mode recognition, in particular to a face recognition method based on blocking consistency LBP (Local Binary Pattern) and sparse coding. The face recognition method comprises the steps of: firstly, segmenting a face image into 16 subdomains which are same in size according to a mode of 4*4, calculating a consistency LBP histogram with one pixel radius and 8 neighbors, connecting LBP histograms of the 16 subdomains into a column vector to be used as a characteristic vector of a face image; and representing images to be tested into a most sparse linear combination on a training set, and recognizing the face image. Compared with the traditional characteristic extraction and clustering algorithm. According to the invention, structure information of a face can be well extracted, and under the condition of a single training sample and shielding, higher recognition rate and robustness are shown.
Description
technical field [0001] The invention belongs to the technical field of digital image processing and pattern recognition, and in particular relates to a face recognition method. Background technique [0002] The biological characteristics studied by biometric identification technology include face, fingerprint, palm print, palm type, iris, retina, vein, voice (speech), body shape, infrared temperature spectrum, ear shape, smell, personal habits (such as typing on the keyboard) strength and frequency, signature, gait), etc., the corresponding recognition technology includes face recognition, fingerprint recognition, palmprint recognition, iris recognition, retina recognition, vein recognition, voice recognition (identification can be carried out by voice recognition, or For the recognition of voice content, only the former belongs to biometric recognition technology), body shape recognition, keyboard tapping recognition, signature recognition, etc. Face recognition specifical...
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