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Abstract: Facial recognition technology is critical in modern security and identification systems, yet it faces significant challenges when dealing with occluded faces, whether due to disguises or in crowds. This review explores the complexities of occluded facial recognition, highlighting the obstacles presented by partial or complete obstruction of facial features by various elements like clothing, accessories, or other people in crowded spaces. These occlusions, intentional or otherwise, complicate the identification process, rendering traditional recognition systems less effective. Recent advancements in machine learning, particularly through Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), have ushered in innovative solutions that enhance the system's ability to recognize faces even when significant portions are obscured. These developments signify a move towards more adaptable and precise occluded facial recognition technologies, essential for real-world applications where occlusions are unpredictable. The paper discusses ongoing challenges and innovations considerations, various occluded system performance metrics and conclude with way ahead for Researchers and Practioners
Singh et al. (Mon,) studied this question.