Noise-resilient face recognition system: A review of denoising approaches and their impact on accuracy

Noise interference and inconsistent image quality pose growing issues for facial recognition systems particularly in urban surveillance settings. While traditional denoising techniques, such as wavelet-based transforms and other classical methods are good at retaining texture, they are not very effective when dealing with complicated noise patterns and high computing demands. Consequently, low-power or embedded applications have found success with lightweight improvements like Local Binary Patterns (LBP). Nonetheless, their limited capacity to interpret high-resolution and color pictures limits their wider use. The advantages and disadvantages of these traditional and contemporary methods are critically examined in this paper, with an emphasis on deep learning-based models like Stacked Denoising Autoencoders (SDAE). Although these models are prone to overfitting and necessitate careful parameter adjustment, they have demonstrated impressive effectiveness in learning noise-robust representations. The study also investigates the possibility of combining stacked autoencoders and Histogram of Oriented Gradients (HoG) as a hybrid approach to get over current bottlenecks. Based on this investigation, a robust denoising framework can be achieved by combining the denoising power of SDAEs with the edge-preserving capabilities of HoG for enhanced feature extraction under structured and mixed noise conditions. This integration is positioned as a future-ready solution for building scalable, real-time, and noise-resilient facial recognition pipelines.