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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 scanned 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

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