mlsanityData to 7 Oct 2026

ChatGPT and biometrics: an assessment of face recognition, gender detection, and age estimation capabilities

arXiv 2403.02965 ↗First posted 5 Mar 2024cs.CV cs.AIcited by 20

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Abstract (arXiv)

This paper explores the application of large language models (LLMs), like ChatGPT, for biometric tasks. We specifically examine the capabilities of ChatGPT in performing biometric-related tasks, with an emphasis on face recognition, gender detection, and age estimation. Since biometrics are considered as sensitive information, ChatGPT avoids answering direct prompts, and thus we crafted a prompting strategy to bypass its safeguard and evaluate the capabilities for biometrics tasks. Our study reveals that ChatGPT recognizes facial identities and differentiates between two facial images with considerable accuracy. Additionally, experimental results demonstrate remarkable performance in gender detection and reasonable accuracy for the age estimation tasks. Our findings shed light on the promising potentials in the application of LLMs and foundation models for biometrics.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 2 of its 17 references that are in the map. First posted on arXiv 5 Mar 2024.

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2 of 17 references.

References · 2

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2017Age Progression/Regression by Conditional Adversarial Autoencodercited by 426

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 4; 2025: 9; 2026: 7.

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  1. 2024ChatGPT Meets Iris Biometricscited by 14
  2. 2025FaceLLM: A Multimodal Large Language Model for Face Understandingcited by 13
  3. 2025Exploring ChatGPT for Face Presentation Attack Detection in Zero and Few-Shot in-Context Learningcited by 12
  4. 2025Foundation versus Domain-specific Models: Performance Comparison, Fusion, and Explainability in Face Recognitioncited by 8
  5. 2024From Pixels to Words: Leveraging Explainability in Face Recognition through Interactive Natural Language Processingcited by 5
  6. 2025Benchmarking Multimodal Large Language Models for Face Recognitioncited by 5
  7. 2026Out of the box age estimation through facial imagery: A Comprehensive Benchmark of Vision-Language Models vs. out-of-the-box Traditional Architecturescited by 5
  8. 2026Evaluating Multimodal Large Language Models for Heterogeneous Face Recognitioncited by 3
  9. 2024CounterFace: A Synthetic Face Dataset for Fine-Grained Counterfactual Evaluation of Face Recognition Systemscited by 2
  10. 2025MADPromptS: Unlocking Zero-Shot Morphing Attack Detection with Multiple Prompt Aggregationcited by 2
  11. 2026MLLM-based Textual Explanations for Face Comparisoncited by 2
  12. 2025Human Re-ID Meets LVLMs: What can we expect?cited by 1
8 more
  1. 2026Demographic Fairness in Multimodal LLMs: A Benchmark of Gender and Ethnicity Bias in Face Verificationcited by 1
  2. 2024Aligning Characteristic Descriptors with Images for Human-Expert-like Explainabilitycited by 0
  3. 2025ChatGPT Encounters Morphing Attack Detection: Zero-Shot MAD with Multi-Modal Large Language Models and General Vision Modelscited by 0
  4. 2025In-context Learning of Vision Language Models for Detection of Physical and Digital Attacks against Face Recognition Systemscited by 0
  5. 2025Trade-offs in Cross-Domain Generalization of Foundation Model Fine-Tuned for Biometric Applicationscited by 0
  6. 2026Generalist Multimodal LLMs Gain Biometric Expertise via Human Saliencecited by 0
  7. 2026Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detectioncited by 0
  8. 2026Benchmarking the Explanatory Quality of Open-Weight Vision-Language Models in Face Recognitioncited by 0