Face rapid recognition method based on t distribution in complex environment

A technology for complex environments and recognition methods, applied in character and pattern recognition, instruments, computer components, etc., can solve the problems of computational complexity and time complexity, efficiency and performance, etc., to ensure speed and improve The effect of recognition accuracy, improved flexibility and rapidity

Inactive Publication Date: 2017-08-04
TONGJI UNIV
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AI Technical Summary

Problems solved by technology

While the network structure tends to be complex, it will inevitably have an impact on the computational complexity

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  • Face rapid recognition method based on t distribution in complex environment
  • Face rapid recognition method based on t distribution in complex environment
  • Face rapid recognition method based on t distribution in complex environment

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[0046] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented on the premise of the technical solution of the present invention, and provides a detailed implementation manner and a specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0047] The flow chart of the fast face recognition method based on t distribution in complex environment is as follows figure 1 shown. The method includes a sample training step and a face recognition step, and the sample training step is specifically:

[0048] A1) Through the convolutional neural network, feature extraction is performed on the training samples to obtain feature samples (x 1 ,x 2 ,…,x n ):

[0049] A11) preprocessing the training samples to obtain an input image of a preset size;

[0050] A12) passing the input image obtained in step A11) through the con...

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Abstract

The invention relates to a face rapid recognition method based on t distribution in a complex environment. The method includes a sample training step and a face recognition step. The sample training step specifically includes: performing feature extraction on a training sample through a convolutional neural network to obtain a feature sample; and projecting the feature sample to a low-dimensional space through t distribution non-linear projection to obtain the training sample in the low-dimensional space and dimension reduction path parameters. The face recognition step specifically includes: performing feature extraction on a to-be-recognized image through the convolutional neural network to obtain a feature sample of the to-be-recognized image; projecting the feature sample r of the to-be-recognized image to the low-dimensional space according to the dimension reduction parameters to obtain a test sample s in the low-dimensional space; and performing classification recognition on the training sample (y1, y2, ..., yn) and the test sample s in the low-dimensional space through a classifier to obtain a recognition result. Compared with the prior art, the method is advantaged by high recognition efficiency, more excellent recognition performance, and high flexibility.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a fast face recognition method based on t-distribution in a complex environment. Background technique [0002] As an application in the field of intelligent recognition, face recognition, based on the unique biometric information of the face, combined with graphic image processing technology and pattern recognition technology to identify and identify people's identities, has high security and reliability, so it has a strong market In recent years, there have been corresponding developments in access control and time attendance, network security, customs border inspection, property management, intelligent identification, driver's license inspection, and computer system login. [0003] In order to achieve the optimal performance of recognition algorithms, research in recent years has clearly recognized that there are still some important challenges in the field of face recogn...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/168G06V40/172G06F18/285G06F18/2136G06F18/214
Inventor 岑峰王澄莹
Owner TONGJI UNIV
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