Summary of Longitudinal Evaluation Of Child Face Recognition and the Impact Of Underlying Age, by Surendra Singh et al.
Longitudinal Evaluation of Child Face Recognition and the Impact of Underlying Age
by Surendra Singh, Keivan Bahmani, Stephanie Schuckers
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a longitudinal approach to improve the reliability of child face recognition technology for various emerging applications. The authors leverage the YFA database, collected by Clarkson University’s CITeR research group over an 8-year period with 6-month intervals, to enhance enrollment and verification accuracy for child faces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to develop a more accurate way to identify children using facial recognition technology. It does this by looking at how well a large database of child faces performs over time. The researchers want to make sure that the technology can correctly recognize children’s faces even after many years, and improve its accuracy for things like verifying identities. |
Keywords
» Artificial intelligence » Face recognition
