Techniques and Approaches of Facial Recognition under Occlusion: A Review

Abstract

A human face is one of the most prominent features used in the process of authenticating technical applications in the domains of security, biometrics, surveillance and forensics. Recognition and detection of facial features has thus become challenging due to problems of occlusion, emotion, image resolution, varying facial expressions and aging. Such attributes tend to have a great impact on the overall performance of a robust facial recognition system. Hence, facial recognition with presence of occlusion triggers to be a hindrance in the natural environment and thereby limits the system model to recognise faces. For this purpose, multiple research authors have inhibited strategies and techniques to address the issues of occlusion. Numerous developments in the field of machine learning and deep learning have constantly evolved with complex architectures that could design the model from scratch and perform image processing to attain maximum efficiency. Such approaches have the potential to accomplish highest state-of-the art accuracy by minimizing error loss. Nevertheless, facial recognition that tends to bypass occlusion is still imperative to limitations for real?world applications. Hence in this review paper, the authors highlight various problems that a facial recognition system with occlusion might face and thereby proposes to analyse various methods of recognition in order to cope with the existing problems. The paper also focuses on extraction approaches thus used present the novelty. The review finally ends, with a mention of future challenges with regards to occluded facial recognition.