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Low-rank matrix and eigenface-based human face identification method

A face recognition, low-rank matrix technology, applied in the field of face recognition based on low-rank matrix and eigenface, can solve the problems of poor recognition effect and large differences in face geometric features

Inactive Publication Date: 2016-11-09
BEIJING UNION UNIVERSITY
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Problems solved by technology

Due to the large differences in the geometric features of the face due to individual differences, the recognition effect is not good

Method used

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  • Low-rank matrix and eigenface-based human face identification method
  • Low-rank matrix and eigenface-based human face identification method
  • Low-rank matrix and eigenface-based human face identification method

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Embodiment Construction

[0068] Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings.

[0069] When the following description refers to the accompanying drawings, the same numerals in different drawings refer to the same or similar elements unless otherwise indicated.

[0070] The implementations described in the following exemplary examples do not represent all implementations consistent with the present invention. Rather, they are merely examples of approaches consistent with aspects of the invention as recited in the appended claims.

[0071] The terminology used in the present invention is for the purpose of describing particular embodiments only, and is not intended to limit the present invention.

[0072] As used herein and in the appended claims, the singular forms "a", "the", and "the" are intended to include the plural forms as well, unless the context clearly dictates otherwise.

[0073] The term "and", "or" used ...

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Abstract

The invention discloses a low-rank matrix and eigenface-based human face identification method, which relates to the technical fields of digital image processing, mode identification, computer vision, physiology and the like, and is used for solving the problem in human face identification based on static images or video images in various scenes. A low-rank matrix is applied to preprocessing of a human face picture based on a low-rank matrix concept, and the influence of changes of illumination, expressions and the like is reduced through low-rank processing on a training picture, so that the algorithm robustness and the identification accuracy are improved. According to the key points of the technical scheme, the method comprises the following steps of firstly acquiring a human face sample picture and establishing a sample library; secondly during a training stage, constructing an eigenvector space through operations of calculating a sample mean value, an eigenvalue, an eigenvector and the like, and projecting the eigenvector to obtain an eigenface; and finally during a test stage, performing PCA projection on a test sample to obtain an eigenvector, calculating a distance between the eigenvector and the eigenface, taking a shortest distance as an identification result, and outputting the identification result.

Description

technical field [0001] The invention is a face recognition method based on a low-rank matrix and eigenfaces, and the method relates to technical fields such as digital image processing, pattern recognition, computer vision, and physiology. Background technique [0002] As an important application of modern biometric technology in life, face recognition can use the visual features of face pictures for identity recognition, and has broad application prospects. Compared with traditional identification methods, face information is difficult to be imitated or forged by others and carried around. Its strong security, confidentiality, and ease of operation make face recognition widely used in real life. Based on the idea of ​​low-rank matrix, the low-rank matrix is ​​applied to the preprocessing of face pictures, and the influence of changes in illumination and expression is weakened by low-rank processing of training pictures, thereby improving the robustness of the algorithm and ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCG06V40/172G06V40/168
Inventor 袁家政赵新超
Owner BEIJING UNION UNIVERSITY
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