CoChat
Sign Up
Cited by 715

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

Annotated Facial Landmarks in the Wild: A large-scale, real-world database for facial landmark localization

Kostinger, Martin, Wohlhart, Paul, Roth, Peter M., Bischof, Horst·2011·2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
DOI 10.1109/iccvw.2011.6130513
Open this paper in CoChat — free account

Abstract

Face alignment is a crucial step in face recognition tasks. Especially, using landmark localization for geometric face normalization has shown to be very effective, clearly improving the recognition results. However, no adequate databases exist that provide a sufficient number of annotated facial landmarks. The databases are either limited to frontal views, provide only a small number of annotated images or have been acquired under controlled conditions. Hence, we introduce a novel database overcoming these limitations: Annotated Facial Landmarks in the Wild (AFLW). AFLW provides a large-scale collection of images gathered from Flickr, exhibiting a large variety in face appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions. In total 25,993 faces in 21,997 real-world images are annotated with up to 21 landmarks per image. Due to the comprehensive set of annotations AFLW is well suited to train and test algorithms for multi-view face detection, facial landmark localization and face pose estimation. Further, we offer a rich set of tools that ease the integration of other face databases and associated annotations into our joint framework.

Overview

Integrity signals

Registry

Crossref · 10.1109/iccvw.2011.6130513

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • For classification schemes to be effective, they need to be tailored to specific populations, which has only been considered by two studies to date.

    “In addition, for classification schemes to be effective, they need to be tailored to specific populations, which has only been considered by two studies to date [58,127].”

    From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [58]

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 28

  1. messer, 1999, XM2VTSDB: The extended M2VTS database, Proc Audio and Video-Based Biometric Person Authentication
  2. 10.1109/ICCV.2009.5459250 · DOI
  3. marti?nez, 1998, The AR face database, Technical Report 24
  4. jesorsky, 2001, Robust face detection using the Hausdorff distance, Proc Audio and Video-Based Biometric Person Authentication, 10.1007/3-540-45344-X_14 · DOI
  5. kasi?nski, 2008, The PUT face database, Image Processing & Communications, 59
  6. huang, 2007, Labeled faces in the wild: A database for studying face recognition in unconstrained environments, Technical Report 07-49
  7. jain, 2010, FDDB: A benchmark for face detection in unconstrained settings, Technical Report UMCS-2010-009, 6
  8. 10.1109/TSMCA.2007.909557 · DOI · Paper page
  9. griffin, 2007, Caltech-256 object category dataset, Technical Report 7694
  10. 10.1109/TPAMI.2008.106 · DOI
  11. milborrow, 2010, The MUCT landmarked face database, Proc Pattern Recognition Assoc of South Africa
  12. aifanti, 2005, The MUG facial expression database, Proc Workshop on Image Analysis for Multimedia Interactive Services
  13. nordstr�m, 2004, The IMM Face Database-an Annotated Dataset of 240 Face Images
  14. 10.1109/CVPR.2007.382969 · DOI
  15. prince, 2011, Probabilistic models for inference about identity, IEEE Trans on PAMI
  16. rowley, 1997, Rotation invariant neural network-based face detection, Technical Report CMUCS-97-201
  17. schneiderman, 2000, A statistical model for 3D object detection applied to faces and cars, Proc CVPR
  18. 10.1109/JPROC.2006.884093 · DOI · Paper page
  19. 10.1109/CVPR.2009.5206513 · DOI
  20. 10.1109/SIU.2007.4298708 · DOI
  21. 10.1109/CVPR.2005.283 · DOI
  22. ahonen, 2004, Face recognition with local binary patterns, Proc ECCV
  23. 10.1109/ICCVW.2009.5457701 · DOI
  24. 10.1007/BF01450852 · DOI
  25. 10.1109/CVPR.2010.5539992 · DOI
  26. 10.1109/ICIP.1997.647401 · DOI
  27. berg, 2004, Names and faces in the news, Proc CVPR
  28. 10.5244/C.20.92 · DOI

Cited by

Sorted by citations

Metadata

Authors
Kostinger, M., Wohlhart, P., Roth, P. M., & Bischof, H.
Venue
2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)
Publisher
IEEE
Published
2011-11
Pages
2144-2151
DOI
10.1109/iccvw.2011.6130513
Cited by
715
References
3

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review