mlsanityData to 7 Oct 2026

ChatGPT Meets Iris Biometrics

arXiv 2408.04868 ↗First posted 9 Aug 2024cs.CVcited by 14

On the map: Multimodal LLMs / vision-language models → MLLM jailbreaks and adversarial robustness

Abstract (arXiv)

This study utilizes the advanced capabilities of the GPT-4 multimodal Large Language Model (LLM) to explore its potential in iris recognition - a field less common and more specialized than face recognition. By focusing on this niche yet crucial area, we investigate how well AI tools like ChatGPT can understand and analyze iris images. Through a series of meticulously designed experiments employing a zero-shot learning approach, the capabilities of ChatGPT-4 was assessed across various challenging conditions including diverse datasets, presentation attacks, occlusions such as glasses, and other real-world variations. The findings convey ChatGPT-4's remarkable adaptability and precision, revealing its proficiency in identifying distinctive iris features, while also detecting subtle effects like makeup on iris recognition. A comparative analysis with Gemini Advanced - Google's AI model - highlighted ChatGPT-4's better performance and user experience in complex iris analysis tasks. This research not only validates the use of LLMs for specialized biometric applications but also emphasizes the importance of nuanced query framing and interaction design in extracting significant insights from biometric data. Our findings suggest a promising path for future research and the development of more adaptable, efficient, robust and interactive biometric security solutions.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Placed in MLLM jailbreaks and adversarial robustness, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 15 of its 37 references that are in the map. First posted on arXiv 9 Aug 2024.

Builds on

15 of 37 references.

References · 15

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  5. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  6. 2023A Survey of Large Language Modelscited by 2,537
  7. 2023PaLM 2 Technical Reportcited by 1,154
  8. 2023Gemini: A Family of Highly Capable Multimodal Modelscited by 810
  9. 2023Capabilities of GPT-4 on Medical Challenge Problemscited by 630
  10. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applicationscited by 428
  11. 2023DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4cited by 128
  12. 2022The AI Teacher Test: Measuring the Pedagogical Ability of Blender and GPT-3 in Educational Dialoguescited by 78
3 more
  1. 2023Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosiscited by 77
  2. 2024ChatGPT and biometrics: an assessment of face recognition, gender detection, and age estimation capabilitiescited by 20
  3. 2023A Systematic Survey of Prompt Engineering on Vision-Language Foundation Modelscited by 12

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 11; 2026: 3.

14 papers.

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  1. 2025Benchmarking Foundation Models for Zero-Shot Biometric Taskscited by 13
  2. 2025Exploring ChatGPT for Face Presentation Attack Detection in Zero and Few-Shot in-Context Learningcited by 12
  3. 2025Foundation versus Domain-specific Models: Performance Comparison, Fusion, and Explainability in Face Recognitioncited by 8
  4. 2025Towards Iris Presentation Attack Detection with Foundation Modelscited by 6
  5. 2026Evaluating Multimodal Large Language Models for Heterogeneous Face Recognitioncited by 3
  6. 2025MADPromptS: Unlocking Zero-Shot Morphing Attack Detection with Multiple Prompt Aggregationcited by 2
  7. 2025Human Re-ID Meets LVLMs: What can we expect?cited by 1
  8. 2025FPBench: A Comprehensive Benchmark of Multimodal Large Language Models for Fingerprint Analysiscited by 1
  9. 2025ChatGPT Encounters Morphing Attack Detection: Zero-Shot MAD with Multi-Modal Large Language Models and General Vision Modelscited by 0
  10. 2025In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systemscited by 0
  11. 2025Can Foundation Models Predict Fitness for Duty?cited by 0
  12. 2025Trade-offs in Cross-Domain Generalization of Foundation Model Fine-Tuned for Biometric Applicationscited by 0
2 more
  1. 2026Generalist Multimodal LLMs Gain Biometric Expertise via Human Saliencecited by 0
  2. 2026Multimodal Models Meet Presentation Attack Detection on ID Documentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024How Good is ChatGPT at Face Biometrics? A First Look into Recognition, Soft Biometrics, and Explainability
  2. 2025Comparative Analysis Based on DeepSeek, ChatGPT, and Google Gemini: Features, Techniques, Performance, Future Prospects
  3. 2023Gemini vs GPT-4V: A Preliminary Comparison and Combination of Vision-Language Models Through Qualitative Cases
  4. 2024An Early Investigation into the Utility of Multimodal Large Language Models in Medical Imaging
  5. 2025Safe-LLaVA: A Privacy-Preserving Vision-Language Dataset and Benchmark for Biometric Safety
  6. 2025Towards Zero-Shot Differential Morphing Attack Detection with Multimodal Large Language Models
  7. 2025Can ChatGPT Perform Image Splicing Detection? A Preliminary Study
  8. 2025RetinalGPT: A Retinal Clinical Preference Conversational Assistant Powered by Large Vision-Language Models