Large Language Models (LLMs) such as GPT developed by OpenAI, have already shown astonishing results, introducing quick changes in our society. This has been intensified by the release of ChatGPT which allows anyone to interact in a simple conversational way with LLMs, without any experience in the field needed. As a result, ChatGPT has been rapidly applied to many different tasks such as code- and song-writer, education, virtual assistants, etc., showing impressive results for tasks for which it was not trained (zero-shot learning). The present study aims to explore the ability of ChatGPT, based on the recent GPT-4 multimodal LLM, for the task of face biometrics. In particular, we analyze the ability of ChatGPT to perform tasks such as face verification, soft-biometrics estimation, and explainability of the results. ChatGPT could be very valuable to further increase the explainability and transparency of the automatic decisions in human scenarios. Experiments are carried out in order to evaluate the performance and robustness of ChatGPT, using popular public benchmarks and comparing the results with state-of-the-art methods in the field. The results achieved in this study show the potential of LLMs such as ChatGPT for face biometrics, especially to enhance explainability. For reproducibility reasons, we release all the code in GitHub.

翻译：由OpenAI开发的GPT等大型语言模型已展现出惊人成果，并迅速引发社会变革。随着ChatGPT的发布，这一趋势进一步加剧——该模型允许任何无需任何领域经验的人以简单对话方式与大型语言模型交互。因此ChatGPT已快速应用于代码创作、歌曲编写、教育、虚拟助手等多项任务，在未经训练的任务（零样本学习）中同样表现卓越。本研究旨在探索基于最新GPT-4多模态大语言模型的ChatGPT在人脸生物识别任务中的能力。具体而言，我们分析了ChatGPT执行人脸验证、软生物识别估计及结果可解释性等任务的能力。ChatGPT对于提升人类场景下自动决策的可解释性与透明度具有重要价值。通过使用公开基准数据集开展实验，我们评估了ChatGPT的性能与鲁棒性，并将结果与该领域最先进方法进行对比。研究结果表明，ChatGPT等大语言模型在人脸生物识别领域具有巨大潜力，尤其在增强可解释性方面。为保障可复现性，我们在GitHub上开源了所有代码。