Face Recognition for Blurry Images Using Deep Learning

The rapid growth of visual surveillance and personal identification systems in smart environments has empowered the development of real-time face recognition, even under imperfect conditions. Among these challenges, recognizing faces in blurry images stands as a significant barrier to achieving consistent identification results. In this paper, we combine advanced machine learning algorithms and deep learning frameworks, integrating Generative Adversarial Networks (GANs), specifically the Generative Facial Prior (GFPGAN), to restore clarity to blurred facial images. The primary objective is to enhance the accuracy of face recognition in real-world scenarios where image quality may be compromised. In the proposed approach, the GFPGAN model serves as the primary mechanism for blind face restoration, transforming blurred images into clear and recognizable visuals. Upon achieving clarity in the image, a customized Convolutional Neural Network (CNN) is deployed to perform face recognition tasks. To showcase the efficiency of the proposed system, a comprehensive comparative analysis is carried out, comparing the results of the pretrained models VGG-Face, FaceNet, CNN, and DeepFace. Our experiments showed a high validation accuracy of 83.72% for FaceNet. Our experimental results demonstrate that the combination of GFPGAN preprocessing and deep learning models significantly improves blurry images face recognition accuracy.