{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/longitudinal-study-of-child-face-recognition","title":"Longitudinal Study of Child Face Recognition","arxiv_id":"1711.03990","date":"2017-11-10","proceeding":null,"authors":["Debayan Deb","Neeta Nain","Anil K. Jain"],"abstract":"We present a longitudinal study of face recognition performance on Children\nLongitudinal Face (CLF) dataset containing 3,682 face images of 919 subjects,\nin the age group [2, 18] years. Each subject has at least four face images\nacquired over a time span of up to six years. Face comparison scores are\nobtained from (i) a state-of-the-art COTS matcher (COTS-A), (ii) an open-source\nmatcher (FaceNet), and (iii) a simple sum fusion of scores obtained from COTS-A\nand FaceNet matchers. To improve the performance of the open-source FaceNet\nmatcher for child face recognition, we were able to fine-tune it on an\nindependent training set of 3,294 face images of 1,119 children in the age\ngroup [3, 18] years. Multilevel statistical models are fit to genuine\ncomparison scores from the CLF dataset to determine the decrease in face\nrecognition accuracy over time. Additionally, we analyze both the verification\nand open-set identification accuracies in order to evaluate state-of-the-art\nface recognition technology for tracing and identifying children lost at a\nyoung age as victims of child trafficking or abduction.","url_abs":"http://arxiv.org/abs/1711.03990v1","url_pdf":"http://arxiv.org/pdf/1711.03990v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"longitudinal-study-of-child-face-recognition","repo_url":"https://github.com/davidsandberg/facenet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"face-recognition","task_name":"Face Recognition"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}