Face Recognition Method Based on Convolutional Neural Network

Abstract

The invention provides a face recognition method based on a convolutional neural network, comprising: performing necessary preprocessing on the face image to obtain an ideal face image; selecting the ideal face image as the input of the convolutional neural network Enter U0, the output of U0 enters UG, and the output of UG is used as the input of US1; the S neuron of US1 extracts the edge components in different directions in the input image through supervised training as the first feature extraction and outputs to the input of UC1 ;The output of UC1 is used as the input of US2, and US2 completes the second feature extraction and is used as the input of UC2; the output of UC2 is used as the input of US3, and US3 completes the third feature extraction and is used as the input of UC3; the output of UC3 is used as US4 US4 obtains the weights, thresholds and the number of neuron cell planes of each layer through supervised competitive learning as the input of UC4; UC4 is used as the output layer of the network, and outputs the final network determined by the maximum output result of US4 Pattern recognition results. The invention can improve the face recognition rate in complex scenes.

Description

technical field [0001] The invention relates to a face recognition method based on a convolutional neural network. Background technique [0002] Face recognition technology is a technology that uses computers to analyze face images, extract effective feature information, and identify personal identities. It first judges whether there is a face in the image? If it exists, the position and size information of each face is further determined. Based on this information, the potential pattern features in each face are further extracted, and compared with the faces in the known face database, so as to identify the category information of each face. Among them, the process of judging whether there is a face in an image is face detection, and the process of comparing the extracted image with the known face database is face recognition. [0003] In recent years, researchers have made a lot of achievements in face detection and face recognition, and the detection performance and re...

AI Technical Summary

Problems solved by technology

[0007] (2) Changes in illumination will change the grayscale information of the face image, which has a great impact on some recognition algorithms based on grayscale features

[0008] (3) The change of expression will also cause the decline of recognition performance

[0009] (4) Face images may also be affected by factors such as age, occlusion, and face image scale, which will affect the performance of face recognition algorithms to varying degrees

Although this method increases the complexity of the neural network structure to a certain extent, the anti-interference performance and recognition rate of the network are greatly improved compared with the former.

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