Foundation Models and Biometrics: A Survey and Outlook

This paper provides an overview of the recent advancements in foundation models and discusses potential applications of these models in the field of biometrics. Foundation models (such as large language models, vision language models, audio-language models, and large multi-modal models) are based on large neural networks which are trained with massive amounts of data and enable robust feature extraction for transfer learning. These models allow efficient zero-shot and few-shot learning, achieving state-of-the-art performance in downstream tasks. Foundation models have been studied and used in different domains, including natural language processing, computer vision, audio processing, and multi-modal processing. Biometrics is also an active field of research, which involves various research problems, ranging from robust recognition to security and privacy in biometric systems. In this paper, we present an in-depth analysis of state-of-the-art methodologies regarding foundation multi-modal models, their advancements, and their applicability to biometrics tasks. We also highlight current limitations and provide insights into potential future research directions in the applications of foundation models in biometrics. To our knowledge, this paper is the first survey which investigates the applications of foundation models in biometrics.