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 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：大型语言模型（LLMs），例如OpenAI开发的GPT，已展现出惊人的成果，并迅速引发社会变革。ChatGPT的发布进一步强化了这一趋势，它允许任何人以简单的对话方式与LLM交互，无需任何领域经验。因此，ChatGPT被迅速应用于许多不同任务，如代码生成、歌词创作、教育、虚拟助手等，并在其未经训练的任务上（零样本学习）表现出令人印象深刻的效果。本研究旨在探索基于最新GPT-4多模态LLM的ChatGPT在人脸生物特征识别任务中的能力。具体而言，我们分析了ChatGPT在人脸验证、软生物特征估计以及结果的可解释性等方面的表现。ChatGPT或可极大提升人机场景中自动化决策的可解释性与透明度。我们使用公开基准数据集开展实验，评估ChatGPT的性能与鲁棒性，并将结果与该领域最新方法进行对比。研究结果表明，ChatGPT等LLM在人脸生物特征识别领域具有潜力，尤其在增强可解释性方面。为确保可复现性，我们在GitHub上发布了所有代码。