  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.
