A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis

2025-08-07

AI Summary

This paper addresses iris-based gender classification—a underexplored yet promising biometric recognition problem—through the first systematic literature review. Covering publications from 2010 to 2023, it analyzes methodological evolution across key stages: iris segmentation, texture modeling (e.g., LBP, Gabor filters), encoding schemes (e.g., BSIF, CORD), and classifier design (traditional SVM/RF vs. deep learning), establishing current performance boundaries. It identifies critical challenges including annotation bias, poor cross-device generalizability, and lack of physiological interpretability. The study proposes three future directions: (i) a modeling paradigm integrating multi-scale texture analysis with anatomical priors; (ii) a lightweight cross-domain adaptive framework; and (iii) an explainability-aware evaluation protocol. By providing the first structured technical analysis and roadmap, this work fills a significant gap in the field and offers both theoretical foundations and practical guidance for developing robust iris-based gender recognition systems.

Technology Category

• Computer Vision: Biometrics, Face, Gesture & Pose
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web text

Abstract

Gender classification is attractive in a range of applications, including surveillance and monitoring, corporate profiling, and human-computer interaction. Individuals' identities may be gleaned from information about their gender, which is a kind of soft biometric.Over the years, several methods for determining a person's gender have been devised. Some of the most well-known ones are based on physical characteristics like face, fingerprint, palmprint, DNA, ears, gait, and iris. On the other hand, facial features account for the vast majority of gender classification methods. Also, the iris is a significant biometric trait because the iris, according to research, remains basically constant during an individual's life. Besides that, the iris is externally visible and is non-invasive to the user, which is important for practical applications. Furthermore, there are already high-quality methods for segmenting and encoding iris images, and the current methods facilitate selecting and extracting attribute vectors from iris textures. This study discusses several approaches to determining gender. The previous works of literature are briefly reviewed. Additionally, there are a variety of methodologies for different steps of gender classification. This study provides researchers with knowledge and analysis of the existing gender classification approaches. Also, it will assist researchers who are interested in this specific area, as well as highlight the gaps and challenges in the field, and finally provide suggestions and future paths for improvement.

Problem

• Evaluates gender classification methods using iris images
• Analyzes existing techniques and identifies research gaps
• Proposes future improvements for iris-based gender classification

Innovation

• Iris image-based gender classification techniques
• Utilizes iris texture attribute vectors
• Reviews existing methodologies and gaps