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The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

300 Faces In-The-Wild Challenge: database and results

Sagonas, Christos, Antonakos, Epameinondas, Tzimiropoulos, Georgios, Zafeiriou, Stefanos, Pantic, Maja·2016·Image and Vision Computing
DOI 10.1016/j.imavis.2016.01.002
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  • Classification schemes are viewed as prone to high inter-observer error.

    “This descriptive approach is preferred as classification schemes are viewed as prone to high inter-observer error [55,59,109].”

    From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [59]

    Section 3.5 (Discussion; page not available) reports high variation among annotators for facial landmarks without clear semantic meaning, particularly the chin, but high agreement for eye and mouth landmarks. It does not assess classification schemes or say that such schemes generally have high inter-observer error. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 17

  1. Cootes, 2001, Active appearance models, IEEE Trans. Pattern Anal. Mach. Intell., 23, 681, 10.1109/34.927467 · DOI · Paper page
  2. Matthews, 2004, Active appearance models revisited, Int. J. Comput. Vis., 60, 135, 10.1023/B:VISI.0000029666.37597.d3 · DOI
  3. Antonakos, 2015, Feature-based Lucas–Kanade and active appearance models, IEEE Trans. Image Process., 24, 2617, 10.1109/TIP.2015.2431445 · DOI
  4. Felzenszwalb, 2005, Pictorial structures for object recognition, Int. J. Comput. Vis., 61, 55, 10.1023/B:VISI.0000042934.15159.49 · DOI
  5. Cootes, 1995, Active shape models—their training and application, Comput. Vis. Image Underst., 61, 38, 10.1006/cviu.1995.1004 · DOI · Paper page
  6. Saragih, 2011, Deformable model fitting by regularized landmark mean-shift, Int. J. Comput. Vis., 91, 200, 10.1007/s11263-010-0380-4 · DOI · Paper page
  7. Cao, 2014, Face alignment by explicit shape regression, Int. J. Comput. Vis., 107, 177, 10.1007/s11263-013-0667-3 · DOI
  8. Gross, 2010, Multi-pie, Image Vis. Comput., 28, 807, 10.1016/j.imavis.2009.08.002 · DOI
  9. Tong, 2012, Semi-supervised facial landmark annotation, Comput. Vis. Image Underst., 116, 922, 10.1016/j.cviu.2012.03.008 · DOI
  10. Tzimiropoulos, 2012, Subspace learning from image gradient orientations, IEEE Trans. Pattern Anal. Mach. Intell., 34, 2454, 10.1109/TPAMI.2012.40 · DOI
  11. Gross, 2005, Generic vs. person specific active appearance models, Image Vis. Comput., 23, 1080, 10.1016/j.imavis.2005.07.009 · DOI
  12. Martinez, 2013, Local evidence aggregation for regression-based facial point detection, IEEE Trans. Pattern Anal. Mach. Intell., 35, 1149, 10.1109/TPAMI.2012.205 · DOI
  13. Čech, 2016, Multi-view facial landmark detection by using a 3D shape model, Image Vis. Comput., 47, 60, 10.1016/j.imavis.2015.11.003 · DOI
  14. Deng, 2016, M3 CSR: multi-view, multi-scale and multi-component cascade shape regression, Image Vis. Comput., 47, 19, 10.1016/j.imavis.2015.11.005 · DOI
  15. Fan, 2016, Approaching human level facial landmark localization by deep learning, Image Vis. Comput., 47, 27, 10.1016/j.imavis.2015.11.004 · DOI
  16. Martinez, 2016, L2,1-based regression and prediction accumulation across views for robust facial landmark detection, Image Vis. Comput., 47, 36, 10.1016/j.imavis.2015.09.003 · DOI
  17. Uřičář, 2016, Multi-view facial landmark detector learned by the structured output SVM, Image Vis. Comput., 47, 45, 10.1016/j.imavis.2016.02.004 · DOI

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Metadata

Authors
Sagonas, C., Antonakos, E., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M.
Venue
Image and Vision Computing
Publisher
Elsevier BV
Published
2016-03
Volume
47
Pages
3-18
ISSN
0262-8856
DOI
10.1016/j.imavis.2016.01.002
Cited by
638
References
1
Language
en
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Funders
  • EPSRC (EP/J017787/1, EP/L026813/1, EP/M02153X/1)
  • European Community Horizon 2020 (645094)

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