Face recognition method and device, electronic equipment and storage medium

Abstract

The invention relates to the field of artificial intelligence, and discloses a face recognition method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-recognized face image, and carrying out the slicing of the face image, and obtaining a plurality of sliced images; coding each slice image by using a coding layer in a pre-trained face recognition model to obtain a plurality of coding vectors; utilizing a multi-head context attention mechanism in the face recognition model to recognize internal associated information and external associated information of each coding vector, and generating image feature information of each slice image according to the internal associated information and the external associated information; and according to the image feature information, identifying the face image by using a classification network in the face identification model to obtain an identification result of the face image. According to the invention, the face recognition efficiency can be improved.

Description

technical field [0001] The present invention relates to the field of artificial intelligence, in particular to a face recognition method, device, electronic equipment and computer-readable storage medium. Background technique [0002] Face recognition, also known as face recognition system, refers to computer technology that uses the analysis and comparison of facial visual feature information for identity identification. Compared with the development of face recognition technology, it has been widely used in various scenes in people's lives, such as Community security, convenient payment and other scenarios. [0003] At present, face recognition is usually implemented based on neural network learning, that is, by optimizing the way of extracting face features to enhance the performance and speed of face recognition, and convolutional neural network is the main means of face feature extraction. The receptive field of the convolutional neural network is determined by the siz...

AI Technical Summary

Problems solved by technology

[0003] At present, face recognition is usually implemented based on neural network learning, that is, by optimizing the way of extracting face features to enhance the performance and speed of face recognition, and convolutional neural network is the main means of face feature extraction. The receptive field of the convolutional neural network is determined by the size of the convolution kernel and the number of channels of the filter, so it is necessary to increase the size of the convolution kernel or the number of channels of the filter to ensure the accuracy of face feature extraction, but This will greatly increase the number of parameters of the model and increase the complexity of model calculation, which will affect the efficiency of face recognition

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