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

arXiv: https://arxiv.org/abs/2203.15203 · HTML: https://mlsanity.com/paper/2203.15203/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 202 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 26 of 26)

- [Masked Face Recognition Dataset and Application](https://mlsanity.com/paper/2003.09093/) (2020)
- [Occlusion Robust Face Recognition Based on Mask Learning with PairwiseDifferential Siamese Network](https://mlsanity.com/paper/1908.06290/) (2019)
- [Masked Face Recognition for Secure Authentication](https://mlsanity.com/paper/2008.11104/) (2020)
- [Masked Face Recognition Challenge: The InsightFace Track Report](https://mlsanity.com/paper/2108.08191/) (2021)
- [Self-restrained Triplet Loss for Accurate Masked Face Recognition](https://mlsanity.com/paper/2103.01716/) (2021)
- [The Effect of Wearing a Mask on Face Recognition Performance: an Exploratory Study](https://mlsanity.com/paper/2007.13521/) (2020)
- [Face Mask Detection using Transfer Learning of InceptionV3](https://mlsanity.com/paper/2009.08369/) (2020)
- [MFR 2021: Masked Face Recognition Competition](https://mlsanity.com/paper/2106.15288/) (2021)
- [A Survey on Periocular Biometrics Research](https://mlsanity.com/paper/1810.03360/) (2018)
- [Grid Loss: Detecting Occluded Faces](https://mlsanity.com/paper/1609.00129/) (2016)
- [Efficient Masked Face Recognition Method during the COVID-19 Pandemic](https://mlsanity.com/paper/2105.03026/) (2021)
- [Periocular Recognition Using CNN Features Off-the-Shelf](https://mlsanity.com/paper/1809.06157/) (2018)
- [Some Research Problems in Biometrics: The Future Beckons](https://mlsanity.com/paper/1905.04717/) (2019)
- [Boosting Masked Face Recognition with Multi-Task ArcFace](https://mlsanity.com/paper/2104.09874/) (2021)
- [Masked Face Recognition Challenge: The WebFace260M Track Report](https://mlsanity.com/paper/2108.07189/) (2021)
- [Ocular Recognition Databases and Competitions: A Survey](https://mlsanity.com/paper/1911.09646/) (2019)
- [Cross-Sensor Periocular Biometrics in a Global Pandemic: Comparative Benchmark and Novel Multialgorithmic Approach](https://mlsanity.com/paper/1902.08123/) (2019)
- [Masked Face Recognition: Human vs. Machine](https://mlsanity.com/paper/2103.01924/) (2021)
- [Probing Fairness of Mobile Ocular Biometrics Methods Across Gender on VISOB 2.0 Dataset](https://mlsanity.com/paper/2011.08898/) (2020)
- [Deep Representations for Cross-spectral Ocular Biometrics](https://mlsanity.com/paper/1911.09509/) (2019)
- [Cross-Spectral Periocular Recognition with Conditional Adversarial Networks](https://mlsanity.com/paper/2008.11604/) (2020)
- [Expression Recognition Using the Periocular Region: A Feasibility Study](https://mlsanity.com/paper/1810.09798/) (2018)
- [Robust Periocular Recognition By Fusing Sparse Representations of Color and Geometry Information](https://mlsanity.com/paper/1309.2752/) (2013)
- [Smartphone Camera De-identification while Preserving Biometric Utility](https://mlsanity.com/paper/2009.08511/) (2020)
- [Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition](https://mlsanity.com/paper/1811.00846/) (2018)
- [Cross-spectral Periocular Recognition: A Survey](https://mlsanity.com/paper/1812.01465/) (2018)

## Built on by (most cited first, 10 of 10)

- [Periocular Biometrics: A Modality for Unconstrained Scenarios](https://mlsanity.com/paper/2212.13792/) (2022)
- [One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations](https://mlsanity.com/paper/2307.05128/) (2023)
- [Combined CNN and ViT features off-the-shelf: Another astounding baseline for recognition](https://mlsanity.com/paper/2407.19472/) (2024)
- [A Survey on Computer Vision based Human Analysis in the COVID-19 Era](https://mlsanity.com/paper/2211.03705/) (2022)
- [Finger-NestNet: Interpretable Fingerphoto Verification on Smartphone using Deep Nested Residual Network](https://mlsanity.com/paper/2212.05884/) (2022)
- [Exploring Complementarity and Explainability in CNNs for Periocular Verification Across Acquisition Distances](https://mlsanity.com/paper/2510.26282/) (2025)
- [Leveraging Large-Scale Face Datasets for Deep Periocular Recognition via Ocular Cropping](https://mlsanity.com/paper/2510.26294/) (2025)
- [Real-Time Drivers' Drowsiness Detection and Analysis through Deep Learning](https://mlsanity.com/paper/2511.12438/) (2025)
- [Periocular Soft Biometrics: A Survey and Applications to Multimedia Forensics and Disinformation Detection](https://mlsanity.com/paper/2608.14701/) (2026)
- [Benchmarking CLIP for Zero-Shot Face and Periocular Gender Estimation](https://mlsanity.com/paper/2610.06102/) (2026)

## Abstract (arXiv)

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.