The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Bridging the gap: from biometrics to forensics
Abstract
Biometric recognition, or simply biometrics, refers to automated recognition of individuals based on their behavioural and biological characteristics. The success of fingerprints in forensic science and law enforcement applications, coupled with growing concerns related to border control, financial fraud and cyber security, has generated a huge interest in using fingerprints, as well as other biological traits, for automated person recognition. It is, therefore, not surprising to see biometrics permeating various segments of our society. Applications include smartphone security, mobile payment, border crossing, national civil registry and access to restricted facilities. Despite these successful deployments in various fields, there are several existing challenges and new opportunities for person recognition using biometrics. In particular, when biometric data is acquired in an unconstrained environment or if the subject is uncooperative, the quality of the ensuing biometric data may not be amenable for automated person recognition. This is particularly true in crime-scene investigations, where the biological evidence gleaned from a scene may be of poor quality. In this article, we first discuss how biometrics evolved from forensic science and how its focus is shifting back to its origin in order to address some challenging problems. Next, we enumerate the similarities and differences between biometrics and forensics. We then present some applications where the principles of biometrics are being successfully leveraged into forensics in order to solve critical problems in the law enforcement domain. Finally, we discuss new collaborative opportunities for researchers in biometrics and forensics, in order to address hitherto unsolved problems that can benefit society at large.
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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 [4]
Citations
Cites
Strengthening Forensic Science in the United States: A Path Forward
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Cited by
Sorted by citations
Error 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
Metadata
- Authors
- Jain, A. K., & Ross, A.
- Affiliations
- Department of Computer Science and Engineering, Michigan State University, East Lansing, MI 48824 USA
- Venue
- Philosophical Transactions of the Royal Society B: Biological Sciences
- Publisher
- The Royal Society
- Published
- 2015-08-05
- Volume
- 370
- Issue
- 1674
- Pages
- 20140254
- ISSN
- 0962-8436
- 1471-2970
- Cited by
- 71
- References
- 1
- Language
- en
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