A survey of face recognition techniques under occlusion

TL;DR

This survey reviews face recognition techniques under occlusion, discussing challenges, existing methods, and future trends to improve recognition accuracy in real-world scenarios with occluded faces.

Contribution

It categorizes and analyzes existing occluded face recognition methods, providing a comprehensive overview and identifying future research directions.

Findings

Classification of methods into three categories

Analysis of strengths and weaknesses of approaches

Discussion of future challenges and trends

Abstract

The limited capacity to recognize faces under occlusions is a long-standing problem that presents a unique challenge for face recognition systems and even for 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 paper, we restrict the scope to occluded face recognition. First, we explore what the occlusion problem is and what inherent difficulties can arise. As a part of this review, we introduce face detection under occlusion, a preliminary step in face recognition. Second, we present how existing face recognition methods cope with the occlusion problem and classify them into three categories, which are 1) occlusion robust feature extraction approaches, 2) occlusion aware face recognition approaches, and 3) occlusion recovery based face recognition approaches. Furthermore, we analyze the motivations, innovations, pros and cons, and the performance of representative approaches for comparison. Finally, future challenges and method trends of occluded face recognition are thoroughly discussed.