Reconstruction and enhancement techniques for overcoming occlusion in facial recognition

Abstract

Facial occlusions in surveillance footage can obscure important features, preventing facial recognition systems from identifying people. This work focuses on reconstructing these missing facial parts using Generative Adversarial Networks (GANs) to improve facial recognition accuracy while maintaining a low false acceptance rate. Additionally, we investigate how the generated images can be further enhanced using various image enhancement methods to boost recognition accuracy. To evaluate the results, we conduct experiments with widely used face embedding models, such as QMagFace and ArcFace, to determine whether image reconstruction and enhancement improve face recognition accuracy.

Facial occlusions in surveillance footage can obscure important features, preventing facial recognition systems from identifying people. This work focuses on reconstructing these missing facial parts using Generative Adversarial Networks (GANs) to improve facial recognition accuracy while maintaining a low false acceptance rate. Additionally, we investigate how the generated images can be further enhanced using various image enhancement methods to boost recognition accuracy. To evaluate the results, we conduct experiments with widely used face embedding models, such as QMagFace and ArcFace, to determine whether image reconstruction and enhancement improve face recognition accuracy.