FaceOracle: Chat with a Face Image Oracle
This work addresses the need for efficient face image quality assessment in document issuance, but it is incremental as it applies existing LLM technology to a specific domain.
The paper tackles the problem of analyzing face image quality for ID and travel documents by introducing FaceOracle, an LLM-powered AI assistant that allows users to interact conversationally to assess and interpret face image quality using standard-compliant algorithms, resulting in enhanced productivity for issuing authorities.
A face image is a mandatory part of ID and travel documents. Obtaining high-quality face images when issuing such documents is crucial for both human examiners and automated face recognition systems. In several international standards, face image quality requirements are intricate and defined in detail. Identifying and understanding non-compliance or defects in the submitted face images is crucial for both issuing authorities and applicants. In this work, we introduce FaceOracle, an LLM-powered AI assistant that helps its users analyze a face image in a natural conversational manner using standard compliant algorithms. Leveraging the power of LLMs, users can get explanations of various face image quality concepts as well as interpret the outcome of face image quality assessment (FIQA) algorithms. We implement a proof-of-concept that demonstrates how experts at an issuing authority could integrate FaceOracle into their workflow to analyze, understand, and communicate their decisions more efficiently, resulting in enhanced productivity.