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Matching live images (``selfies'') to images from ID documents is a problemthat can arise in various applications. A challenging instance of the problemarises when the face image on the ID document is from early adolescence and thelive image is from later adolescence. We explore this problem using a privatedataset called Chilean Young Adult (CHIYA) dataset, where we match live faceimages taken at age 18-19 to face images on ID documents created at ages 9 to18. State-of-the-art deep learning face matchers (e.g., ArcFace) haverelatively poor accuracy for document-to-selfie face matching. To achievehigher accuracy, we fine-tune the best available open-source model with tripletloss for a few-shot learning. Experiments show that our approach achieveshigher accuracy than the DocFace+ model recently developed for this problem.Our fine-tuned model was able to improve the true acceptance rate for the mostdifficult (largest age span) subset from 62.92% to 96.67% at a false acceptancerate of 0.01%. Our fine-tuned model is available for use by other researchers.

Identity Document to Selfie Face Matching Across Adolescence