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

Abstract

The present invention proposes a face recognition method based on the fusion of Maxout multi-convolutional neural networks, which solves the technical problem of uneven classification of positive and negative samples by a single network. The implementation steps are: use regression tree combination algorithm and affine transformation to process training data Obtain the aligned database; extract the face images in the aligned database to obtain a database with only face area images; construct two networks, and use the Maxout method to obtain the fused network; use the database with only face area images to train the fused convolutional neural network The network obtains the trained network model; after the test image is preprocessed, the trained network model is tested. The present invention adopts the Maxout module to fuse two or more subsystems, and then sequentially connects to the fully connected module and the output module to obtain a complete face recognition system. The invention can well balance the classification sensitivity of the convolutional neural network to positive and negative samples, and improve the face recognition accuracy.

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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