Face rapid recognition method based on t distribution in complex environment

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...

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 and time complexity, so there is still a problem that it is difficult to achieve both efficiency and performance.

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