Maxout multi-convolution neural network fusion face recognition method and system

Abstract

The invention provides a Maxout multi-convolution neural network fusion face recognition method. The technical problem of unequal classification of positive and negative samples in a single network is solved. The method comprises the steps that a regression tree combination algorithm and affine transformation processing training data are used to acquire a database after alignment; face images in the aligned database are extracted to acquire a database simply with face area images; two networks are built, and a Maxout method is used to acquire a fused network; the database simply with face area images is used to train the fused convolution nerve network to acquire a trained network model; and a test image is pre-processed to test the trained network model. According to the invention, a Maxout module is used to fuse two or more subsystems, and then is successively connected with a full connection module and an output module to acquire a complete face recognition system; the classification sensitivity of positive and negative samples of the convolution neural network can be balanced; and the face recognition accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of image processing, relates to convolutional neural network and face recognition technology, specifically a face recognition method and system based on Maxout multi-convolutional neural network fusion, which can be used for video retrieval, dynamic monitoring, identity recognition, Intelligent buildings and other fields. Background technique [0002] As a natural attribute of human beings, human face is a biological feature with great differences and easy acquisition. Therefore, face recognition technology has received extensive attention and research. Face recognition specifically refers to an authentication technology that analyzes and judges face images through computers and related algorithms. Face recognition technology is widely used. For example, in the field of public security criminal investigation, fugitives can be arrested by following the face recognition system at airports and stations; face r...

AI Technical Summary

Problems solved by technology

Although the rapid development of convolutional neural network has brought great breakthroughs to face recognition, there are still many problems to be solved and overcome in the face recognition technology based on a single convolutional neural network, such as lighting, background and posture. It has an impact on the final result; different network structures perform differently on the same test set. Some convolutional neural networks perform well on negative sample pairs (two images that do not belong to the same person), and some convolutional neural networks perform well on negative sample pairs (two images that do not belong to the same person). The neural network performs well on positive pairs (two images belonging to the same person)

[0004] To sum up, the existing face recognition method based on a single convolutional neural network, due to its uneven classification of positive and negative samples, leads to low face recognition test accuracy

However, the existing face recognition methods based on multi-convolutional neural network fusion mostly use simple serial fusion or linear fusion, which leads to an increase in network parameters, increases network training time, and easily causes network overfitting.

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