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

2026-08-10

AI Summary

This study addresses the challenges of occluded identity recognition and the scarcity of soft biometric cues in synthetic media detection by presenting a systematic review of periocular soft biometric estimation techniques. By examining state-of-the-art advancements in handcrafted descriptors and deep learning architectures for gender, age, and ethnicity prediction, this work delineates application pathways in multimedia forensics, surveillance filtering, and anti-disinformation efforts. Beyond integrating cross-domain应用场景, the review critically highlights pivotal challenges regarding data bias, algorithmic fairness, and cross-domain generalization. Consequently, this comprehensive synthesis not only consolidates current knowledge on periocular analysis but also establishes a strategic roadmap for future research, emphasizing the necessity of robust and equitable biometric systems in increasingly complex visual environments.

Abstract

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.

Problem

• Periocular Soft Biometrics
• Demographic Attribute Estimation
• Multimedia Forensics
• Disinformation Detection

Innovation

• Periocular Soft Biometrics
• Multimedia Forensics
• Disinformation Detection
• Demographic Attribute Estimation
• Deep Learning