Identity Document to Selfie Face Matching Across Adolescence
This addresses a challenging face matching problem for applications like identity verification, but it is incremental as it fine-tunes existing models on a specific dataset.
The paper tackles the problem of matching ID document face images from early adolescence to selfies from later adolescence, achieving a true acceptance rate improvement from 62.92% to 96.67% at a false acceptance rate of 0.01% for the most difficult age span.
Matching live images (``selfies'') to images from ID documents is a problem that can arise in various applications. A challenging instance of the problem arises when the face image on the ID document is from early adolescence and the live image is from later adolescence. We explore this problem using a private dataset called Chilean Young Adult (CHIYA) dataset, where we match live face images taken at age 18-19 to face images on ID documents created at ages 9 to 18. State-of-the-art deep learning face matchers (e.g., ArcFace) have relatively poor accuracy for document-to-selfie face matching. To achieve higher accuracy, we fine-tune the best available open-source model with triplet loss for a few-shot learning. Experiments show that our approach achieves higher accuracy than the DocFace+ model recently developed for this problem. Our fine-tuned model was able to improve the true acceptance rate for the most difficult (largest age span) subset from 62.92% to 96.67% at a false acceptance rate of 0.01%. Our fine-tuned model is available for use by other researchers.