Longitudinal Evaluation of Child Face Recognition and the Impact of Underlying Age
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Abstract (arXiv)
The need for reliable identification of children in various emerging applications has sparked interest in leveraging child face recognition technology. This study introduces a longitudinal approach to enrollment and verification accuracy for child face recognition, focusing on the YFA database collected by Clarkson University CITeR research group over an 8 year period, at 6 month intervals.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 9 of its 21 references that are in the map. First posted on arXiv 1 Aug 2024.
Builds on
9 of 21 references.
References · 9
- 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,833
- 2017VGGFace2: A dataset for recognising faces across pose and agecited by 911
- 2016Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networkscited by 895
- 2021MagFace: A Universal Representation for Face Recognition and Quality Assessmentcited by 332
- 2017Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environmentscited by 258
- 2021Face.evoLVe: A High-Performance Face Recognition Librarycited by 26
- 2017Longitudinal Study of Child Face Recognitioncited by 18
- 2022Face Recognition In Children: A Longitudinal Studycited by 7
- 2021Longitudinal Analysis of Mask and No-Mask on Child Face Recognitioncited by 4
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.