Face recognition method based on convolutional neural network

Abstract

The invention provides a face recognition method based on a convolutional neural network. The face recognition method comprises the following steps: carrying out necessary pretreatment at early stage on a facial image to obtain an ideal facial image; selecting the ideal facial image as the input of the convolutional neural network to enter U0, wherein the output of the U0 enters UG, and the output of the UG is taken as the input of US1; extracting edge components in different directions in an input image as first-time feature extraction and outputting to the input of special UC1 through supervised training by the S nerve cell of the US1; taking the output of the UC1 as the input of US2, completing the second-time feature extraction by the US2 and taking as the input of UC2; taking the output of the UC2 as the input of US3, and completing the third-time feature extraction by the US3 and taking as the input of UC3; taking the output of the UC3 as the input of US4, and obtaining the weight, threshold value and neuron plane number in each layer in a supervision competitive learning mode by the US4 and taking as the input of the UC4; taking the UC4 as the output layer of the network, and outputting the final mode recognition result of the network determined by the maximum output result of the US4. According to the face recognition method, the recognition rate of faces in complex scenarios can be improved.

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. And based on these 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 performance of detectio...

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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