A survey of face recognition techniques under occlusion

Abstract

The limited capacity to recognise faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even humans. The problem regarding occlusion is less covered by research when compared to other challenges such as pose variation, different expressions, etc. Nevertheless, occluded face recognition is imperative to exploit the full potential of face recognition for real-world applications. In this article, the scope to occluded face recognition is restricted and a systematic categorisation that new as well as classic methods fit into is presented. First, the authors explore the kind of the occlusion problem and the type of inherent difficulties that can arise. As a part of this review, face detection under occlusion, a preliminary step in face recognition. Second the authors analyse how the existing face recognition methods cope with the occlusion problem and classify them into three categories, which are given as: 1) occlusion robust feature extraction approaches, 2) occlusion aware face recognition approaches, and 3) occlusion recovery based face recognition approaches. Furthermore, the motivations, innovations, pros and cons, and the performance of representative approaches for comparison are analyzed. Finally, future challenges and method trends of occluded face recognition are thoroughly discussed.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Masked Face Recognition Using Deep Learning: A Review

“…Zeng et al [26] reviewed the existing face recognition methods that only consider the occluded faces. They categorized the evaluated approaches into three main phases, which are the occlusion feature extraction, occlusion detection, and occlusion recovery and recognition.…”

“…Face masks were only used in this survey as examples of objects challenging the recognition system of facial expressions. Moreover, deep learning approaches were among six Exhaustive surveys on FR [20][21][22][23][24][25] and OFR [3,[26][27][28] have been published in recent years. These studies have set standard algorithmic pipelines and highlighted many important challenges and research directions.…”

Comparative Study Based on De-Occlusion and Reconstruction of Face Images in Degraded Conditions

“…The third one is head pose variations, which introduce self-occlusion and projective deformations [11]. Finally, partial occlusion can be engendered by an object such as a cap or scarf, or even a face can be occluded by another face in a crowd of people [12]. Such challenges cause many degradations and lack of information, which leads to a fall in the recognition rate.…”

Indian Masked Faces in the Wild Dataset

“…It is now used in various applications such as surveillance, access control, forensics, and e-payments and researchers have been enhancing the state-of-the-art performance on challenging tasks [5,6,7,8]. However, the problem of recognizing faces under partial or heavy occlusion is still considered a challenging task [9]. As the world is facing the pandemic of COVID-19, people worldwide are wearing masks as a protective measure.…”