ChatGPT and biometrics: an assessment of face recognition, gender detection, and age estimation capabilities (2403.02965v2)
Abstract: This paper explores the application of 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.
- “Chatgpt in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations,” Frontiers in Artificial Intelligence, vol. 6, pp. 1169595, 2023.
- “Chatgpt applications in academic research: A review of benefits, concerns, and recommendations,” bioRxiv, pp. 2023–08, 2023.
- “Chatgpt in healthcare: a taxonomy and systematic review,” Computer Methods and Programs in Biomedicine, p. 108013, 2024.
- “Exploring the role of chatgpt in medical image analysis,” Biomedical Signal Processing and Control, vol. 86, pp. 105292, 2023.
- “Image analysis through the lens of chatgpt-4,” Journal of Applied Artificial Intelligence, vol. 4, no. 2, 2023.
- “Revolutionizing radiology with gpt-based models: Current applications, future possibilities and limitations of chatgpt,” Diagnostic and Interventional Imaging, vol. 104, no. 6, pp. 269–274, 2023.
- “Gpt-4 technical report,” arXiv preprint arXiv:2303.08774, 2023.
- “Labeled faces in the wild: A database for studying face recognition in unconstrained environments,” Tech. Rep. Technical Report 07-49, University of Massachusetts, Amherst, October 2007.
- “Agedb: the first manually collected, in-the-wild age database,” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 14, no. 1s, 2017.
- “Frontal to profile face verification in the wild,” in IEEE Winter Conference on Applications of Computer Vision (WACV), 2016.
- “Mobiface: A lightweight deep learning face recognition on mobile devices,” in 2019 IEEE 10th international conference on biometrics theory, applications and systems (BTAS). IEEE, 2019, pp. 1–6.
- Maciej Grączyński, “Biggest genderface recognition dataset,” https://www.kaggle.com/datasets/maciejgronczynski/biggest-genderface-recognition-dataset, Accessed: January 2024.
- “E2f-gan: Eyes-to-face inpainting via edge-aware coarse-to-fine gans,” IEEE Access, vol. 10, pp. 32406–32417, 2022.
- “Synthetic face generation through eyes-to-face inpainting,” in IEEE International Joint Conference on Biometrics (IJCB 2023).
- “Hyperextended lightface: A facial attribute analysis framework,” in 2021 International Conference on Engineering and Emerging Technologies (ICEET). IEEE, 2021, pp. 1–4.
- “Age progression/regression by conditional adversarial autoencoder,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 5810–5818.
- Ahmad Hassanpour (6 papers)
- Yasamin Kowsari (4 papers)
- Hatef Otroshi Shahreza (18 papers)
- Bian Yang (5 papers)
- Sebastien Marcel (77 papers)