Application of Image Enhancement Techniques in Facial Recognition System

Abstract

Face recognition becomes an important biometric implementation in any surveillance, access control, forensic and intelligent security system applications. Despite of technical advances in deep learning models, recognition accuracy normally affected by facial pose, illumination, occlusion, expression, aging factors and limited availability of labelled dataset. These challenges reduce the robustness of face recognition models in real world environments [1][2][3]. Recent researches demonstrated that the super-resolution techniques based on Generative Adversarial Networks (GANs) reconstruct high-quality facial images very effectively from low-resolution input images. It supports to improve feature representation and face recognition [4][5]. Therefore, this research motivated from this advancement and proposes an Adaptive Super-Resolution Generative Adversarial Network (Adaptive SRGAN) for face recognition. It integrates adaptive learning with image super resolution to reconstruct identity preserving high resolution facial images by employing adaptive learning rate optimization, dynamic loss weighting, attention guided feature enhancement and identity preserving loss functions.  However, it enhances reconstruction quality by preserving discriminative facial characteristics [6]. The proposed model is expected to achieve higher Peak Signal to Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), recognition accuracy, precision, recall, and F1-score while reducing false acceptance and false rejection rates. Simultaneously, Adaptive SRGAN delivers a robust and scalable solution for improving face recognition systems.