发表机构
Halmstad University（哈尔姆斯塔德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究综述了眼周图像的人口统计属性估计方法，探讨其在多媒体取证与虚假信息检测中的应用，并指出数据集偏差等未解决挑战。
AI 中文摘要
软生物特征属性（如性别、年龄、种族）在无法进行完整身份识别时，可提供有价值的辅助证据，支持法医调查、身份验证、监控或合成及篡改媒体检测等应用。在生物特征模态中，眼周区域是软生物特征线索的可靠来源，因为当面部其他部位被遮挡时，眼周区域通常仍可见——这在法医证据和监控录像中是常见情况，且可在多种采集条件下被捕获。本文对基于眼周图像的人口统计属性估计进行了综述，涵盖公开可用数据集、从手工描述符到深度学习架构的方法趋势，以及性别、年龄和种族预测的最新技术水平。我们讨论了与多媒体取证和虚假信息检测相关的用例，包括监控录像中的人口统计过滤、年龄验证以及合成数据中的人口统计不一致性检测。我们还强调了未解决的挑战，包括数据集偏差、跨域泛化、公平性、伦理方面以及缺乏面向法医的基准。
英文摘要
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.
CommentsAccepted for publication at ECCV 2026 Workshop on AI for Multimedia Forensics & Disinformation Detection (AI4MFDD2026)
