Literature DB >> 26101280

Bridging the gap: from biometrics to forensics.

Anil K Jain1, Arun Ross2.   

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.
© 2015 The Author(s) Published by the Royal Society. All rights reserved.

Entities:  

Keywords:  biometrics; fingermarks; forensics; sketch-to-photo matching; tattoo matching; video surveillance

Mesh:

Year:  2015        PMID: 26101280      PMCID: PMC4580999          DOI: 10.1098/rstb.2014.0254

Source DB:  PubMed          Journal:  Philos Trans R Soc Lond B Biol Sci        ISSN: 0962-8436            Impact factor:   6.237


  4 in total

1.  Automatic comparison of finger-ridge patterns.

Authors:  M TRAURING
Journal:  Nature       Date:  1963-03-09       Impact factor: 49.962

2.  The impact of human-technology cooperation and distributed cognition in forensic science: biasing effects of AFIS contextual information on human experts.

Authors:  Itiel E Dror; Kasey Wertheim; Peter Fraser-Mackenzie; Jeff Walajtys
Journal:  J Forensic Sci       Date:  2011-12-28       Impact factor: 1.832

3.  Latent fingerprint matching.

Authors:  Anil K Jain; Jianjiang Feng
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  2011-01       Impact factor: 6.226

4.  Repeatability and reproducibility of decisions by latent fingerprint examiners.

Authors:  Bradford T Ulery; R Austin Hicklin; JoAnn Buscaglia; Maria Antonia Roberts
Journal:  PLoS One       Date:  2012-03-12       Impact factor: 3.240

  4 in total
  3 in total

1.  Perceptual expertise in forensic facial image comparison.

Authors:  David White; P Jonathon Phillips; Carina A Hahn; Matthew Hill; Alice J O'Toole
Journal:  Proc Biol Sci       Date:  2015-09-07       Impact factor: 5.349

2.  Error Rates in Users of Automatic Face Recognition Software.

Authors:  David White; James D Dunn; Alexandra C Schmid; Richard I Kemp
Journal:  PLoS One       Date:  2015-10-14       Impact factor: 3.240

3.  Recognition of a Person Wearing Sport Shoes or High Heels through Gait Using Two Types of Sensors.

Authors:  Marcin Derlatka; Mariusz Bogdan
Journal:  Sensors (Basel)       Date:  2018-05-21       Impact factor: 3.576

  3 in total

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