# Periocular Soft Biometrics: A Survey and Applications to Multimedia Forensics and Disinformation Detection

arXiv: https://arxiv.org/abs/2608.14701 · HTML: https://mlsanity.com/paper/2608.14701/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 74 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Builds on (most cited first, 20 of 20)

- [Demographic Bias in Biometrics: A Survey on an Emerging Challenge](https://mlsanity.com/paper/2003.02488/) (2020)
- [A Survey on Periocular Biometrics Research](https://mlsanity.com/paper/1810.03360/) (2018)
- [50 Years of Automated Face Recognition](https://mlsanity.com/paper/2505.24247/) (2025)
- [Ocular Recognition Databases and Competitions: A Survey](https://mlsanity.com/paper/1911.09646/) (2019)
- [On the Effect of Selfie Beautification Filters on Face Detection and Recognition](https://mlsanity.com/paper/2110.08934/) (2021)
- [Periocular Biometrics and its Relevance to Partially Masked Faces: A Survey](https://mlsanity.com/paper/2203.15203/) (2022)
- [Periocular Biometrics: A Modality for Unconstrained Scenarios](https://mlsanity.com/paper/2212.13792/) (2022)
- [Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images](https://mlsanity.com/paper/2103.16760/) (2021)
- [A New Periocular Dataset Collected by Mobile Devices in Unconstrained Scenarios](https://mlsanity.com/paper/2011.12427/) (2020)
- [Probing Fairness of Mobile Ocular Biometrics Methods Across Gender on VISOB 2.0 Dataset](https://mlsanity.com/paper/2011.08898/) (2020)
- [Predicting Gender from Iris Texture May Be Harder Than It Seems](https://mlsanity.com/paper/1811.10066/) (2018)
- [Attribute-Based Deep Periocular Recognition: Leveraging Soft Biometrics to Improve Periocular Recognition](https://mlsanity.com/paper/2111.01325/) (2021)
- [Relevant features for Gender Classification in NIR Periocular Images](https://mlsanity.com/paper/1904.12007/) (2019)
- [Gender and Ethnicity Classification of Iris Images using Deep Class-Encoder](https://mlsanity.com/paper/1710.02856/) (2017)
- [Recognizing Gender from Human Facial Regions using Genetic Algorithm](https://mlsanity.com/paper/1712.01661/) (2017)
- [Harnessing Unlabeled Data to Improve Generalization of Biometric Gender and Age Classifiers](https://mlsanity.com/paper/2110.04427/) (2021)
- [Analyzing Covariate Influence on Gender and Race Prediction from Near-Infrared Ocular Images](https://mlsanity.com/paper/1805.01912/) (2018)
- [Periocular Recognition in the Wild with Orthogonal Combination of Local Binary Coded Pattern in Dual-stream Convolutional Neural Network](https://mlsanity.com/paper/1902.06383/) (2019)
- [Sex-Prediction from Periocular Images across Multiple Sensors and Spectra](https://mlsanity.com/paper/1905.00396/) (2019)
- [OcularAge: A Comparative Study of Iris and Periocular Images for Pediatric Age Estimation](https://mlsanity.com/paper/2505.05374/) (2025)

## Abstract (arXiv)

Soft-biometric attributes such as gender, age, and ethnicity provide valuable ancillary evidence when full identity recognition is not feasible, supporting applications in forensic investigation, identity verification, surveillance, or detection of synthetic and manipulated media. Among biometric modalities, the periocular region is a robust source of soft-biometric cues, as it often remains visible when other parts of the face are occluded, a frequent condition in forensic evidence and surveillance footage, and can be captured across a wide range of acquisition conditions. In this paper, we provide a survey of demographic attribute estimation from periocular images, covering publicly available datasets, methodological trends from handcrafted descriptors to deep learning architectures, and the state of the art in gender, age, and ethnicity prediction. We discuss use cases relevant to multimedia forensics and disinformation-detection applications, including demographic filtering in surveillance footage, age verification, and the detection of demographic inconsistencies in synthetic data. We also highlight open challenges, including dataset bias, cross-domain generalisation, fairness, ethical aspects, and the lack of forensic-oriented benchmarks.