Deep learning face recognition system and method based on self-attention mechanism

Abstract

The invention discloses a deep learning face recognition system and a deep learning face recognition method based on a self-attention mechanism, and belongs to the field of computer vision and mode recognition. According to the invention, a channel self-attention module is constructed. The dimension conversion transposition is carried out on three-dimensional data of a feature map. A cross-correlation relationship matrix between channels is learned to represent the relative relationship between different channels, the optimized features of the channels are obtained through calculation according to original features, different weight assignment is carried out on the different channels, selection of channel filtering is achieved, and redundant information of the feature channels is reduced.A space self-attention module is constructed; modeling the spatial information of the three-dimensional feature map; learning a cross-correlation relationship matrix between the spatial positions of the feature map; according to the method, the spatial position of the face feature map is optimized to represent the relative relation between different positions, the features after spatial position optimization are obtained through calculation with the input features, different positions of the face feature map are endowed with different weights, selection of important feature areas of the face is achieved, and the features are concentrated in the important areas of the face.

Description

technical field [0001] The invention belongs to the field of computer vision and pattern recognition, and more specifically relates to a deep learning face recognition system and method based on a self-attention mechanism. Background technique [0002] In recent years, with the rapid development of computer parallel computing and processing capabilities, the technical field of computer vision has made great progress driven by the upsurge of deep learning, and has certain application requirements in various fields. Face recognition, as a visual algorithm that enables computers to automatically identify the identity of relevant personnel in monitoring data, is widely used in various fields such as intelligent security, personnel attendance, community inspections, and self-service. For example, the SkyEye monitoring system in my country's "Safe City Smart Community" plan uses face recognition technology to track and arrest suspects; in our daily life and work, face recognition ...

AI Technical Summary

Problems solved by technology

[0006] In view of the shortcomings and improvement needs of the general convolutional neural network in the prior art, such as the high number of feature map channels leading to overfitting and the fact that differ

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