The DOI matches its Crossref record.
Face Matching and Retrieval in Forensics Applications
Abstract
This article surveys forensic face-recognition approaches and the challenges they face in improving matching and retrieval results as well as processing low-quality images.
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Integrity signals
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Claims the paper makes
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?
Automatic face recognition systems are not entirely automatic; human operators review candidate lists and check for the presence or absence of matching identities.
“it is important to note that Automatic Face Recognition (FR) systems do not yet live up to their name—they are not entirely automatic.”
From Error Rates in Users of Automatic Face Recognition Software (2015) · cites this paper as [3]
Citations
Cites
No references could be resolved yet.
Research works printed but not linked: 18
- 10.1109/TPAMI.2010.180 · DOI
- 10.1109/TIFS.2010.2049842 · DOI
- 10.1109/TIFS.2011.2156787 · DOI
- 2011, Los Angeles Officials Identify Video Assault Suspects, New York Times
- klare, 2011, Heterogeneous Face Recognition using Kernel Prototype Similarities, MSU Technical Report MSU-CSE-11–2
- 10.1109/TIFS.2009.2038751 · DOI
- 10.1109/TPAMI.2008.222 · DOI
- tuthill, 2002, Individualization Principles and Procedures in Criminalistics
- 10.1109/BTAS.2010.5634533 · DOI
- charette, 2011, Here's Looking at You, and You, and You, IEEE Spectrum Blog
- grother, 2010, Report on the Evaluation of 2D Still-Image Face Recognition Algorithms, Nat'l Inst of Standards and Technology Interagency/Internal Report (NISTIR) 7709
- 10.1109/TPAMI.2003.1227983 · DOI
- 10.1109/COGINF.2010.5599727 · DOI
- 10.1109/TPAMI.2010.14 · DOI
- 10.1007/978-3-540-74549-5_113 · DOI
- 10.1007/978-0-85729-932-1 · DOI
- vlahos, 2009, Surveillance Society: New High-Tech Cameras Are Watching You, Popular Mechanics
- 10.1109/TIFS.2011.2109951 · DOI
Cited by
Sorted by citations
Face Recognition Performance: Role of Demographic Information
Klare, B. F., Burge, M. J., Klontz, J. C., Vorder Bruegge, R. W., & Jain, A. K. (2012) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2012.2214212 · Cited by 516
“, resolution, compression, blur), time lapse (facial aging), and occlusion also contribute to face recognition errors [2].”
Self-citationError Rates in Users of Automatic Face Recognition Software
White, D., Dunn, J. D., Schmid, A. C., & Kemp, R. I. (2015) · PLOS ONE · doi:10.1371/journal.pone.0139827 · Cited by 141
Forensic Facial Comparison: Current Status, Limitations, and Future Directions
Bacci, N., Davimes, J. G., Steyn, M., & Briers, N. (2021) · Biology · doi:10.3390/biology10121269 · Cited by 26
Validation of forensic facial comparison by morphological analysis in photographic and CCTV samples
Bacci, N., Houlton, T. M. R., Briers, N., & Steyn, M. (2021) · International Journal of Legal Medicine · doi:10.1007/s00414-021-02512-3 · Cited by 23
Metadata
- Authors
- Jain, A. K., Klare, B., & Park, U.
- Venue
- IEEE Multimedia
- Published
- 2012-01
- Volume
- 19
- Issue
- 1
- Pages
- 20-20
- ISSN
- 1070-986X
- Cited by
- 105
- References
- 0
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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.
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