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

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arxiv 2403.02965 v2 pith:WPZMZME6 submitted 2024-03-05 cs.CV cs.AI

classification cs.CVcs.AI
keywords chatgptbiometricstaskscapabilitiesdetectionestimationgenderaccuracy
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FaceLLM: A Multimodal Large Language Model for Face Understanding

    cs.CV 2025-07 conditional novelty 5.0 of 10

    Fine-tuning InternVL3 on ChatGPT-generated face QA pairs yields a face-specialized MLLM with the highest reported accuracy among MLLMs on FaceXBench.

  2. Human Re-ID Meets LVLMs: What can we expect?

    cs.CV 2025-01 conditional novelty 4.0 of 10

    On a curated 20-query subset of Market1501, PersonViT strongly outperforms ChatGPT-4o, Gemini-2.0-Flash, Claude 3.5 Sonnet, and Qwen-VL-Max in separating genuine from impostor person matches.

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