@inproceedings{jain-etal-2025-pixels,
    title = "From Pixels to Prompts: Evaluating {C}hat{GPT}-4o in Face Recognition, Age Estimation, and Gender Classification",
    author = "Jain, Jashn  and
      Chandaliya, Praveen Kumar  and
      Sharma, Dhruti P.",
    editor = "Das, Sudhansu Bala  and
      Mishra, Pruthwik  and
      Singh, Alok  and
      Muhammad, Shamsuddeen Hassan  and
      Ekbal, Asif  and
      Das, Uday Kumar",
    booktitle = "Proceedings of the Workshop on Beyond English: Natural Language Processing for all Languages in an Era of Large Language Models",
    month = sep,
    year = "2025",
    address = "Varna, Bulgaria",
    publisher = "INCOMA Ltd., Shoumen, BULGARIA",
    url = "https://preview.aclanthology.org/corrections-2026-01/2025.globalnlp-1.16/",
    pages = "141--148",
    abstract = "This study investigates the biometric capabilities of ChatGPT-4o, evaluating its performance on age estimation, gender classification, and identity verification across two challenging datasets: the ITWCC (images of children aged 6{--}17) and a pediatric surgery dataset. By leveraging tailored prompts that bypass safety filters, ChatGPT-4o outperformed conventional CNN-based models such as DeepFace, achieving higher accuracy and offering interpretable, rationale-rich outputs. Specifically, it delivered a mean absolute error of 1.8 years in age estimation, 96{--}100{\%} gender classification accuracy, and over 85{\%} identity continuity recognition, even across surgical transformations. The findings demonstrate the potential of multimodal LLMs to complement or exceed traditional approaches in face analysis tasks, though the study notes the importance of expanding demographic diversity, refining prompt strategies, and ensuring fairness and robustness in real-world settings."
}