Face to face: Comparing ChatGPT with human performance on face matching.

Abstract

ChatGPT's large language model, GPT-4V, has been trained on vast numbers of image-text pairs and is therefore capable of processing visual input. This model operates very differently from current state-of-the-art neural networks designed specifically for face perception and so I chose to investigate whether ChatGPT could also be applied to this domain. With this aim, I focussed on the task of face matching, that is, deciding whether two photographs showed the same person or not. Across six different tests, ChatGPT demonstrated performance that was comparable with human accuracies despite being a domain-general 'virtual assistant' rather than a specialised tool for face processing. This perhaps surprising result identifies a new avenue for exploration in this field, while further research should explore the boundaries of ChatGPT's ability, along with how its errors may relate to those made by humans.