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Cited by 22

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Automated Analysis of Craniofacial Morphology Using Magnetic Resonance Images

Chakravarty, M. Mallar, Aleong, Rosanne, Leonard, Gabriel, Perron, Michel, Pike, G. Bruce, Richer, Louis, Veillette, Suzanne, Pausova, Zdenka, Paus, Tomáš·2011·PLoS ONE
DOI 10.1371/journal.pone.0020241PMC PMC3105012
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Crossref · 10.1371/journal.pone.0020241

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3 of 3 references verified0 citation errors · 0 not checkable

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  • The human face undergoes significant anatomical change during childhood, and this change is quantitatively and qualitatively different from changes occurring during adulthood.

    “The human face undergoes significant anatomical change during childhood, and the change is both quantitatively [24] and qualitatively [25] different to changes occurring during adulthood.”

    From Error Rates in Users of Automatic Face Recognition Software (2015) · cites this paper as [25]

    The Results (Voxel-by-voxel analysis of deformation fields) quantify significant age-related facial changes in adolescents. They do not compare the magnitude of those changes with changes during adulthood. The citing sentence assigns the qualitative comparison to a different citation [25], so that part is not attributed to this paper. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • Infants' faces change substantially at a young age.

    “infants’ faces change substantially at a young age (Chakravarty et al., 2011; Farkas et al., 1992; Ferrario et al., 1999)”

    From Unfamiliar face matching with photographs of infants and children (2018) · cites this paper as Chakravarty et al., 2011

  • Accelerated growth of facial structure in childhood and adolescence increases task difficulty for humans, as previously shown for machine recognition.

    “This suggests that accelerated growth of facial structure in childhood and adolescence [24, 25] increases task difficulty for humans; as has previously been shown for machine recognition [27].”

    From Error Rates in Users of Automatic Face Recognition Software (2015) · cites this paper as [25]

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Metadata

Authors
Chakravarty, M. M., Aleong, R., Leonard, G., Perron, M., Pike, G. B., Richer, L., Veillette, S., Pausova, Z., & Paus, T.
Venue
PLoS ONE
Publisher
Public Library of Science (PLoS)
Published
2011-05-31
Volume
6
Issue
5
Pages
e20241
ISSN
1932-6203
DOI
10.1371/journal.pone.0020241
PMC
PMC3105012
Cited by
22
References
3
Language
en

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