Cited by 7

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

Development of the Wits Face Database: an African database of high-resolution facial photographs and multimodal closed-circuit television (CCTV) recordings

Bacci, Nicholas, Davimes, Joshua, Steyn, Maryna, Briers, Nanette·2021·F1000Research

Abstract

Forensic facial comparison is a commonly used, yet under-evaluated method employed in medicolegal contexts across the world. Testing the accuracy and reliability of facial comparisons requires large scale controlled and matching facial image databases. Databases that contain images of individuals on closed-circuit television (CCTV), with matching formal and informal photographs are needed for this type of research. Although many databases are available, the majority if not all are developed in order to improve facial recognition and face detection algorithms through machine learning, with very limited if any measure of standardisation. This paper aims to review the available databases and describe the development of a high resolution, standardised facial photograph and CCTV recording database of male Africans. The database is composed of a total of 6220 standardised and uncontrolled suboptimal facial photographs of 622 matching individuals in five different views, as well as corresponding CCTV footage of 334 individuals recorded under different realistic conditions. A detailed description of the composition and acquisition process of the database as well as its subdivisions and possible uses are provided. The challenges and limitations of developing this database are also highlighted, particularly with regard to obtaining CCTV video recordings and ethics for a database of faces. The application process to access the database is also briefly described.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.12688/f1000research.50887.1

Cites

1 of 1 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • During data transfer, any interruptions or fluctuations in local area network traffic or connectivity would result in data loss or irreparable corruption.

    “During data transfer, any interruptions or fluctuations in local area network traffic or connectivity would result in data loss or irreparable corruption [41].”

    From Forensic Facial Comparison: Current Status, Limitations, and Future Directions (2021) · cites this paper as [41]

    The Challenges and limitations section says that any interruption or fluctuation in local area network speeds or connectivity would result in data loss or corruption during transfer from IP cameras to a central server. It does not say the corruption was irreparable, so that qualifier is unsupported. No page number is provided in our copy. (LLM, conf=0.85)

    Experimental — accuracy not yet measured
  • Approximately 21.2% loss of IP CCTV data and approximately 3.6% loss of analogue CCTV data occurred during the establishment of the Wits Face Database.

    “Our studies [41] found approximately 21.2% loss of IP CCTV data and approximately 3.6% loss of analogue CCTV data during the establishment of the Wits Face Database (Table 1).”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 36

  1. I Dror, 2006, Contextual information renders experts vulnerable to making erroneous identifications., Forensic Sci Int., 156, 74-8, 10.1016/j.forsciint.2005.10.017 · DOI · Paper page
  2. C Speckeis, 2011, Can ACE-V be validated?, J Forensic Identif., 61, 201-9
  3. M Steyn, 2018, Forensic facial comparison in South Africa: State of the science., Forensic Sci Int., 287, 190-4, 10.1016/j.forsciint.2018.04.006 · DOI · Paper page
  4. T Valentine, 2015, Forensic Facial Identification., Forensic Facial Identification., 1-347
  5. R Gross, 2005, Face Databases, 301-27, 10.1007/0-387-27257-7_14 · DOI
  6. L Karam, 2015, Quality labeled faces in the wild (QLFW): a database for studying face recognition in real-world environments., Hum Vis Electron Imaging XX., 9394, 93940B, 10.1117/12.2080393 · DOI · Paper page
  7. C Sagonas, 2015, 300 Faces In-The-Wild Challenge: database and results., Image Vis Comput., 47, 3-18, 10.1016/j.imavis.2016.01.002 · DOI · Paper page
  8. T Sim, 2003, The CMU Pose, Illumination, and Expression (PIE) database., IEEE Trans Pattern Anal Mach Intell., 1615-8, 10.1109/TPAMI.2003.1251154 · DOI
  9. P Belhumeur, 2013, Localizing parts of faces using a consensus of exemplars., IEEE Trans Pattern Anal Mach Intell., 35, 2930-40, 10.1109/TPAMI.2013.23 · DOI
  10. X Burgos-Artizzu, 2013, Robust face landmark estimation under occlusion., Proc IEEE Int Conf Comput Vis., 1513-20, 10.1109/ICCV.2013.191 · DOI
  11. N Tottenham, 2009, The NimStim set of facial expressions: Judgments from untrained research participants., Psychiatry Res., 168, 242-9, 10.1016/j.psychres.2008.05.006 · DOI · Paper page
  12. N Gourier, 2004, Estimating face orientation from robust detection of salient facial structures., International Workshop on Visual Observation of Deicitic Gestures., 17-25
  13. M Kostinger, 2011, Annotated Facial Landmarks in the Wild: a large-scale, real-world database for facial landmark localization., 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)., 2144-51, 10.1109/ICCVW.2011.6130513 · DOI · Paper page
  14. S Pigeon, 1997, The M2VTS Multimodal Face Database., First Int Conf Audio-and Video-based Biometric Pers Authentication., 403-9
  15. M Calvo, 2008, Facial expressions of emotion (KDEF): Identification under different display-duration conditions., Behav Res Methods., 40, 109-15, 10.3758/brm.40.1.109 · DOI
  16. P Belhumeur, 1997, Eigenfaces vs. Fisherfaces : Recognition Using Class Specific Linear Projection., IEEE Trans Pattern Anal Mach Intell., 711-20, 10.1109/34.598228 · DOI
  17. A Georghiades, 2001, From few to many: Illumination cone models for face recognition under variable lighting and pose., IEEE Trans Pattern Anal Mach Intell., 23, 643-60, 10.1109/34.927464 · DOI
  18. M Lyons, 1998, Coding facial expressions with Gabor wavelets., Proc - 3rd IEEE Int Conf Autom Face Gesture Recognition, FG 1998., 200-5, 10.1109/AFGR.1998.670949 · DOI
  19. M Grgic, 2011, SCface - Surveillance cameras face database., Multimed Tools Appl., 51, 863-79, 10.1007/s11042-009-0417-2 · DOI
  20. D Ma, 2015, The Chicago face database: A free stimulus set of faces and norming data., Behav Res Methods., 47, 1122-35, 10.3758/s13428-014-0532-5 · DOI · Paper page
  21. M La Cascia, 2002, Fast, reliable head tracking under varying illumination: An approach based on registration of texture-mapped 3D models., Pattern Analysis and Machine Intelligence, IEEE Transactions on., 22, 322-36, 10.1109/34.845375 · DOI
  22. A Dhall, 2007, Collecting Large, Richly Annotated Facial-Expression Databases from Movies., J LaTeX Cl Files., 6, 1-14
  23. J Shen, 2016, The First Facial Landmark Tracking in-The-Wild Challenge: Benchmark and Results., IEEE International Conference on Computer Vision., 1003-11, 10.1109/ICCVW.2015.132 · DOI
  24. M Ariz, 2016, A novel 2D/3D database with automatic face annotation for head tracking and pose estimation., Comput Vis Image Underst., 148, 201-10, 10.1016/j.cviu.2015.04.009 · DOI
  25. A Senior, 2011, Privacy Protection and Face Recognition, 671-91, 10.1007/978-0-85729-932-1_27 · DOI
  26. J Phillips, 2000, The FERET evaluation methodology for face-recognition algorithms., 22, 1090-1104, 10.1109/34.879790 · DOI · Paper page
  27. P Phillips, 2006, Preliminary face recognition grand challenge results., 15-21, 10.1109/FGR.2006.87 · DOI
  28. W Gao, 2008, The CAS-PEAL large-scale chinese face database and baseline evaluations., IEEE Trans Syst Man Cybern Syst., 38, 149-61
  29. F Samaria, 1994, Parameterisation of a stochastic model for human face identification., IEEE Work Appl Comput Vis - Proc., 138-42, 10.1109/ACV.1994.341300 · DOI
  30. P Urbanová, 2018, Introducing the FIDENTIS 3D Face Database., Anthropol Rev., 81, 202-23, 10.2478/anre-2018-0016 · DOI
  31. V Le, 2012, Interactive Facial Feature Localization., 679-92, 10.1007/978-3-642-33712-3_49 · DOI
  32. O Jesorsky, 2001, Robust face detection using the Hausdorff distance., 90-5, 10.1007/3-540-45344-X_14 · DOI
  33. E Marszalec, 2000, Physics-based face database for color research., J Electron Imaging., 9, 32-28, 10.1117/1.482722 · DOI
  34. M Steyn, 2007, Interpretation of ante-mortem stature estimates in South Africans., Forensic Sci Int., 171, 97-102, 10.1016/j.forsciint.2006.10.006 · DOI
  35. C Stephan, 2015, Perspective distortion in craniofacial superimposition: Logarithmic decay curves mapped mathematically and by practical experiment., Forensic Sci Int., 257, 520.e1-520.e8, 10.1016/j.forsciint.2015.09.009 · DOI · Paper page
  36. R Finn, 2013, Seven Types of Privacy, 3-32, 10.1007/978-94-007-5170-5_1 · DOI · Paper page

Cited by

Sorted by citations

Metadata

Authors
Bacci, N., Davimes, J., Steyn, M., & Briers, N.
Venue
F1000Research
Published
2021-02-19
Volume
10
Pages
131
ISSN
2046-1402
Cited by
7
References
1
Language
en
Funders
  • National Research Foundation (11858, CSUR160425163022)
  • J.J.J. Smieszeck Fellowship, School of Anatomical Sciences, University of the Witwatersrand

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