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Super-resolution face recognition method based on deep belief networks

A face recognition and super-resolution technology, applied in the field of face recognition, can solve problems such as performance degradation

Inactive Publication Date: 2013-01-30
DALIAN UNIV OF TECH
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Problems solved by technology

This method still uses linear extraction factors in feature extraction, and its canonical correlation analysis is also a linear transformation method. When there is a large attitude change, the performance of this method is greatly reduced.

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  • Super-resolution face recognition method based on deep belief networks
  • Super-resolution face recognition method based on deep belief networks
  • Super-resolution face recognition method based on deep belief networks

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Embodiment Construction

[0016] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific examples. These examples are illustrative only and not limiting of the invention.

[0017] The present invention proposes a super-resolution face recognition method based on deep trust network, which method may include:

[0018] a. Restricted Boltzmann machine. Restricted Boltzmann machine is a Markov random field or a double-layer graph structure, a special structure of Boltzmann machine. As shown in Figure 1, the picture (a) is a general Boltzmann machine, and the Boltzmann machine is a full-rank two-layer graph structure. The lower layer can be called the visible layer, and the upper layer can be called the hidden layer. (b) is a restricted Boltzmann machine. Compared with the general Boltzmann machine, the restricted Boltzmann machine does not allo...

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Abstract

The invention discloses a super-resolution face recognition method based on deep belief networks, and relates to the technical field of face recognition. From a cognitive perspective, it is believed that an intrinsic relation exists between mutually corresponding face images differing in resolutions. But previous studies show that the method for expressing the intrinsic relation by linear approximation is restricted by linear approximation. Therefore, it is believed that the intrinsic relation is nonlinear. In view of outstanding performances of an artificial neural network on the nonlinear classification problem, a neural network algorithm is adopted to capture the nonlinear relation of the mutually corresponding face images differing in resolutions under the condition of posture change. Both theoretical research and neurophysiological research show that a deep structure, such as a system constructed by multiple layers of nonlinear processing units, should be constructed to build an intelligent processing system. According to the face recognition method, deep belief networks are adopted to extract a common nonlinear structure shared by mutually corresponding face images differing in resolutions.

Description

technical field [0001] The invention relates to the technical field of face recognition, and relates to a super-resolution face recognition method based on a deep trust network. Background technique [0002] Face recognition is an important biometric authentication technology and one of the most important problems in computational vision and pattern recognition. In recent decades, researchers have proposed a large number of methods, which have been widely used in security systems such as video surveillance. However, due to the limitations of distance and hardware conditions, the resolution of the face images of interest captured in large-scene video surveillance systems is often relatively low, thereby reducing the performance of face recognition. How to improve the recognition effect under low-resolution conditions is a problem that needs to be solved in face recognition at present. [0003] Image super-resolution (SR) refers to the use of an algorithm to obtain one or a ...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
Inventor 樊鑫林妙真
Owner DALIAN UNIV OF TECH
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