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Face recognition method, system and storage medium based on semi-non-negative matrix decomposition of e-auxiliary function

A face recognition system and semi-non-negative matrix technology, applied in the field of data processing, can solve the problems of narrow application range of non-negative matrix decomposition algorithm, improved effect and convergence speed, etc.

Active Publication Date: 2019-03-22
SHENZHEN UNIV
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Problems solved by technology

[0025] 1. The non-negative matrix factorization algorithm (NMF) has a narrow application range and is only suitable for non-negative data
[0026] 2. Although the traditional semi-non-negative matrix factorization algorithm (Semi-NMF) has expanded the application range of the non-negative matrix factorization algorithm, its effect and convergence speed still need to be improved

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  • Face recognition method, system and storage medium based on semi-non-negative matrix decomposition of e-auxiliary function

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Embodiment Construction

[0065] The present invention proposes the new concept of the E auxiliary function of the objective function for the first time, and accordingly proposes a new basic theory and framework for constructing auxiliary functions, which greatly expands the selection range of auxiliary functions, and also allows us to flexibly construct auxiliary functions It provides a powerful tool to design new high-performance non-negative feature algorithms. According to the method of E auxiliary function proposed by the present invention, we construct a new auxiliary function, and deduce a new fast semi-nonnegative matrix factorization (FSNMF) algorithm accordingly. It can be seen from the properties of the auxiliary function that the FSNMF algorithm obtained by the present invention is convergent. Experimental results show that the FSNMF iterative method proposed by the present invention has better recognition rate and faster convergence speed than other NMF-based algorithms. At the same time,...

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Abstract

The invention provides a face recognition method based on semi-non-negative matrix decomposition of E auxiliary function, Systems and storage media, The face recognition method comprises the followingsteps: transforming The training sample image into a training sample matrix, Setting the error threshold, Maximum number of iterations, and inputting training sample matrix, error threshold and maximum number of iterations; 2, initializimg a base image matrix and a coefficient matrix; Fourth step: Updating the base image matrixand the coefficient matrix according to Equation Step 6: Determining whether the objective function or the number of iterations n reaches the maximum number of iterations, If so, the output base image matrix and the coefficient matrix, otherwise performing the fourth step. The face recognition method of the invention has the advantages of high recognition performance and low computational complexity, and the result shows that the method developed by the patent has certain superiority by comparing with the related algorithm in the open face database.

Description

technical field [0001] The invention relates to the technical field of data processing, in particular to a face recognition method, system and storage medium based on semi-nonnegative matrix decomposition of an E auxiliary function. Background technique [0002] With the advent of the information age, biometrics, which uses the inherent physiological and behavioral characteristics of the human body for personal identification, has become one of the most active research fields. Among the many branches of biometric technology, the most easily accepted technology is face recognition technology, because compared with other biometric technologies, face recognition is non-invasive, non-mandatory, and non-contact. and concurrency. [0003] Face recognition technology consists of two stages. The first stage is feature extraction, which is to extract the face feature information in the face image. This stage directly determines the quality of face recognition technology; the second ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/168G06V40/172G06F18/213G06F18/214
Inventor 陈文胜陈海涛
Owner SHENZHEN UNIV