ChatGPT and biometrics: an assessment of face recognition, gender detection, and age estimation capabilities
This work addresses the potential use of LLMs for biometric applications, which is an incremental advancement in leveraging existing models for new tasks.
This paper tackled the problem of applying large language models like ChatGPT to biometric tasks such as face recognition, gender detection, and age estimation, using a crafted prompting strategy to bypass safeguards, and found that ChatGPT achieved considerable accuracy in face recognition, remarkable performance in gender detection, and reasonable accuracy in age estimation.
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.