Longitudinal Evaluation of Child Face Recognition and the Impact of Underlying Age
<|reference_start|>Longitudinal Evaluation of Child Face Recognition and the Impact of Underlying Age: 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.<|reference_end|>
@article{singh2024longitudinal,
    title={Longitudinal Evaluation of Child Face Recognition and the Impact of
  Underlying Age},
    author={Surendra Singh, Keivan Bahmani, Stephanie Schuckers},
    journal={arXiv preprint arXiv:2408.07225},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.07225},
    primaryClass={cs.CV cs.AI}
}