Segmentation and Multimodal Fusion in Ocular Biometric Recognition: A Review
TL;DR
This review focuses on ocular region segmentation as an essential component of biometric recognition rather than an isolated semantic segmentation task, and categorizes multimodal ocular fusion strategies into three levels— data-, feature-, and decision-level fusion—and compares their information preservation, alignment requirements, computational complexity, and deployment suitability.
Abstract
Accurate segmentation of ocular regions is a fundamental prerequisite for robust ocular biometric recognition, as it affects feature extraction, multimodal representation, and subsequent matching under unconstrained conditions. This review focuses on ocular region segmentation as an essential component of biometric recognition rather than an isolated semantic segmentation task. We survey major segmentation paradigms, including traditional image-processing methods, deep learning architectures, and emerging foundation-model-based approaches, and analyze their applications in delineating the iris, sclera, pupil, and periocular structures under challenges such as occlusion, illumination variation, specular reflection, and motion blur. We further categorize multimodal ocular fusion strategies into three levels—data-, feature-, and decision-level fusion—and compare their information preservation, alignment requirements, computational complexity, and deployment suitability. To facilitate reproducible evaluation, we review representative public datasets and benchmark protocols, summarizing their acquisition characteristics, annotation properties, and evaluation metrics. Finally, we discuss practical considerations for method selection and highlight persistent challenges, including image-quality degradation, cross-spectral and cross-sensor domain shifts, limited dataset diversity, and cross-modal misalignment. Future research directions are outlined toward standardized, quality-aware, efficient, and deployable ocular biometric recognition systems. Received: 5 April 2026 | Revised: 30 June 2026 | Accepted: 27 July 2026 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement No new data were generated or collected in this review article. This study is based exclusively on previously published literature and publicly available ocular biometric datasets. The datasets mentioned in this review, including iris, sclera, pupil, and periocular image databases, can be accessed through the original publications or repositories maintained by their respective dataset providers. The authors did not create, modify, or redistribute any dataset during this study. Author Contribution Statement Yansuo Yu: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – review & editing, Supervision, Project administration, Funding acquisition. Yongbin Qi: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization. Shilin Zhao: Validation, Formal analysis, Investigation, Resources, Data curation, Visualization. Huanzhang Qi: Validation, Formal analysis, Investigation, Resources, Data curation, Visualization. Wendao Li: Resources, Visualization. Haoqi Zhang: Resources, Visualization. Da Teng: Validation, Formal analysis, Resources, Supervision, Project administration. Qiang Liu: Supervision, Project administration, Funding acquisition.