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Cited by 19

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Face Recognition for Newborns, Toddlers, and Pre-School Children: A Deep Learning Approach

Siddiqui, Sahar, Vatsa, Mayank, Singh, Richa·2018·2018 24th International Conference on Pattern Recognition (ICPR)
DOI 10.1109/icpr.2018.8545742
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Abstract

Biometric recognition of newborns, toddlers, and pre-school children aims is an important research challenge with applications in identifying newborn swapping, missing kids, and disbursing benefits. In this research, we propose a representation learning algorithm to extract unique and invariant features from face images of newborns and toddlers, to design an efficient face recognition algorithm. Specifically, we propose a deep learning model which applies class-based penalties while learning the filters of a convolutional neural network. The proposed CNN architecture achieves a rank-1 identification accuracy of 62.7% for single gallery newborn face recognition and 85.1% for single gallery toddler face recognition, forming state-of-the-results for both the databases. Comparison with several existing algorithms also showcases the effectiveness of the proposed algorithm on both the databases.

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Crossref · 10.1109/icpr.2018.8545742

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2 of 2 references verified0 citation errors · 0 not checkable

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  • Siddiqui et al.'s CNN architecture achieves a rank-1 identification accuracy of 62.7% for single gallery newborn face recognition and 85.1% for single gallery toddler FR.

    “The CNN architecture proposed in the study achieves a rank-1 identification accuracy of 62.7% for single gallery newborn face recognition and 85.1% for single gallery toddler FR.”

    From Longitudinal Evaluation of Child Face Recognition and the Impact of Underlying Age (2024) · cites this paper as [15]

Citations

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Flagged first, then by citations

Research works printed but not linked: 17

  1. 10.1109/BTAS.2017.8272750 · DOI
  2. 0, Aadhar card Registration for newborns starts in two state hospitals
  3. galton, 1899, Finger prints of young children, Report of the British Association for the Advancement of Science, 69, 868
  4. parkhi, 2015, Deep face recognition, British Machine Vision Conference
  5. 10.1162/jocn.1991.3.1.71 · DOI · Paper page
  6. 10.1109/34.598228 · DOI
  7. 10.1109/TPAMI.2006.244 · DOI
  8. 10.1109/CVPR.2015.7298682 · DOI · Paper page
  9. 10.1109/TIFS.2016.2639346 · DOI
  10. 10.1007/s00521-011-0530-9 · DOI
  11. weingaertner, 2008, Newborn's biometric identification: Can it be done?, 3rd International Conference on Computer Vision Theory and Applications, 1, 200
  12. 10.1109/TIFS.2016.2538744 · DOI
  13. 10.1109/BTAS.2010.5634500 · DOI
  14. 10.1109/BIOSIG.2016.7736912 · DOI
  15. 10.1109/BTAS.2014.6996299 · DOI · Paper page
  16. 10.1109/TIFS.2014.2318433 · DOI
  17. 10.1109/TIFS.2017.2668221 · DOI

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Metadata

Authors
Siddiqui, S., Vatsa, M., & Singh, R.
Venue
2018 24th International Conference on Pattern Recognition (ICPR)
Publisher
IEEE
Published
2018-08
Pages
3156-3161
DOI
10.1109/icpr.2018.8545742
Cited by
19
References
2

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