Research on Deep Learning Face Recognition System Based on Adaptive Attention Mechanism
Facial recognition has become an indispensable part of social life,commerce and public services.Due to the com-plexity and diversity of facial images,their accuracy and recognition speed require in-depth research.This article aims to optimize the YOLOv5 model and build a fast,automated and efficient facial recognition system.Firstly,a feature library normalization func-tion is added to the input end of the model to achieve uniformity and maximization of input image target features.Then,an adaptive attention mechanism is designed to better focus on target features of different sizes,optimize the loss function to improve overall per-formance.A feature comparison module is designed to achieve facial recognition.Finally,a visual interface is designed to increase the maneuverability and objectivity of the system.The model is trained to use the Wider Face dataset,and after optimization,the recall rate of the model increased by 2.2%and mAP increased by 2.4%,which is higher than the original YOLOv5s model.The improved facial recognition system achieves an overall recognition effect of 87.65%,providing an effective method for automated conference attendance systems.