# A Self-Supervised Adversarial Deblurring Face Recognition Network for Edge Devices

## TL;DR

This paper introduces a facial recognition model for edge devices that improves accuracy in blurry and dynamic scenarios using adversarial learning and deblurring techniques.

## Contribution

The novel contribution is a self-supervised adversarial deblurring network optimized for edge devices with enhanced performance in dynamic and blurry conditions.

## Key findings

- The model achieved an average recall rate of 87.40% on facial recognition datasets.
- It demonstrated accuracy rates of 81.06% and 79.77% on YTF and WiderFace datasets, respectively.
- The model effectively handles blurry and dynamic images in human activity recognition.

## Abstract

With the advancement of information technology, human activity recognition (HAR) has been widely applied in fields such as intelligent surveillance, health monitoring, and human–computer interaction. As a crucial component of HAR, facial recognition plays a key role, especially in vision-based activity recognition. However, current facial recognition models on the market perform poorly in handling blurry images and dynamic scenarios, limiting their effectiveness in real-world HAR applications. This study aims to construct a fast and accurate facial recognition model based on novel adversarial learning and deblurring theory to enhance its performance in human activity recognition. The model employs a generative adversarial network (GAN) as the core algorithm, optimizing its generation and recognition modules by decomposing the global loss function and incorporating a feature pyramid, thereby solving the balance challenge in GAN training. Additionally, deblurring techniques are introduced to improve the model’s ability to handle blurry and dynamic images. Experimental results show that the proposed model achieves high accuracy and recall rates across multiple facial recognition datasets, with an average recall rate of 87.40% and accuracy rates of 81.06% and 79.77% on the YTF, IMDB-WIKI, and WiderFace datasets, respectively. These findings confirm that the model effectively addresses the challenges of recognizing faces in dynamic and blurry conditions in human activity recognition, demonstrating significant application potential.