Periocular Biometrics and its Relevance to Partially Masked Faces: A Survey

TL;DR

This survey reviews periocular biometrics, emphasizing its importance for face recognition when masks are worn, covering techniques, datasets, applications, and future challenges in the context of masked face recognition.

Contribution

It provides a comprehensive overview of periocular biometrics, including recent techniques, datasets, and evaluation methods specifically for recognizing masked faces.

Findings

Periocular biometrics is effective for masked face recognition.

Fusion with other modalities enhances recognition accuracy.

Several datasets and competitions facilitate research in this area.

Abstract

The performance of face recognition systems can be negatively impacted in the presence of masks and other types of facial coverings that have become prevalent due to the COVID-19 pandemic. In such cases, the periocular region of the human face becomes an important biometric cue. In this article, we present a detailed review of periocular biometrics. We first examine the various face and periocular techniques specially designed to recognize humans wearing a face mask. Then, we review different aspects of periocular biometrics: (a) the anatomical cues present in the periocular region useful for recognition, (b) the various feature extraction and matching techniques developed, (c) recognition across different spectra, (d) fusion with other biometric modalities (face or iris), (e) recognition on mobile devices, (f) its usefulness in other applications, (g) periocular datasets, and (h) competitions organized for evaluating the efficacy of this biometric modality. Finally, we discuss various challenges and future directions in the field of periocular biometrics.