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Unobtrusive user identification with light biometrics

Published: 23 October 2004 Publication History

Abstract

Biometric methods are used for recognition and verification of the identity of a person in many applications. Certain concerns over the obtrusive nature of their use, threats to privacy and even the danger of identity theft are rising. In this paper unobtrusive and privacy preserving light biometrics, such as height, weight, and body fat percentage are suggested for user identification. An experiment with 62 test subjects was conducted. In verification type of application total error rate of 11% was achieved using weight data alone and fusion with height data reduced the error rate to 2.4%. With a short list of five best scoring identities the percentage of cases with the correct identity on the list was 90% for weight alone and 100% for the combination of weight and height. The application domain for light biometrics is seen in non-security applications, such as homes, small offices and health clubs.

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Cited By

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  • (2023)All about that BASE: Modeling Biometric Authentication Systems and their Evaluations to enable a more efficient Exchange of Research Results2023 IEEE International Joint Conference on Biometrics (IJCB)10.1109/IJCB57857.2023.10448589(1-10)Online publication date: 25-Sep-2023
  • (2019)SAX2SEX: Gender Classification on 3D Faces using Symbolic Aggregate ApproXimation2019 6th International Conference on Image and Signal Processing and their Applications (ISPA)10.1109/ISPA48434.2019.8966849(1-5)Online publication date: Nov-2019
  • (2018)Biometric Authentication using Soft Biometric Traits2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC)10.1109/PDGC.2018.8745766(259-264)Online publication date: Dec-2018
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cover image ACM Other conferences
NordiCHI '04: Proceedings of the third Nordic conference on Human-computer interaction
October 2004
472 pages
ISBN:1581138571
DOI:10.1145/1028014
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2004

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Author Tags

  1. biometrics
  2. body fat percentage
  3. height
  4. identification
  5. identity theft
  6. privacy
  7. verification
  8. weight

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NordiCHI04
NordiCHI04: NordiCHI 2004
October 23 - 27, 2004
Tampere, Finland

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Overall Acceptance Rate 379 of 1,572 submissions, 24%

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Cited By

View all
  • (2023)All about that BASE: Modeling Biometric Authentication Systems and their Evaluations to enable a more efficient Exchange of Research Results2023 IEEE International Joint Conference on Biometrics (IJCB)10.1109/IJCB57857.2023.10448589(1-10)Online publication date: 25-Sep-2023
  • (2019)SAX2SEX: Gender Classification on 3D Faces using Symbolic Aggregate ApproXimation2019 6th International Conference on Image and Signal Processing and their Applications (ISPA)10.1109/ISPA48434.2019.8966849(1-5)Online publication date: Nov-2019
  • (2018)Biometric Authentication using Soft Biometric Traits2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC)10.1109/PDGC.2018.8745766(259-264)Online publication date: Dec-2018
  • (2016)What Else Does Your Biometric Data Reveal? A Survey on Soft BiometricsIEEE Transactions on Information Forensics and Security10.1109/TIFS.2015.248038111:3(441-467)Online publication date: Mar-2016
  • (2015)Integration of multiple soft biometrics for human identificationPattern Recognition Letters10.5555/2852783.285302268:P2(278-287)Online publication date: 15-Dec-2015
  • (2014)Soft Biometrics; Human Identification Using Comparative DescriptionsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2013.21936:6(1216-1228)Online publication date: 1-Jun-2014
  • (2013)Interpreting “You” and “Me”Digital Democracy and the Impact of Technology on Governance and Politics10.4018/978-1-4666-3637-8.ch003(20-37)Online publication date: 2013
  • (2013)Soft Biometrics for Surveillance: An OverviewHandbook of Statistics - Machine Learning: Theory and Applications10.1016/B978-0-444-53859-8.00013-8(327-352)Online publication date: 2013
  • (2013)Multimodal Biometric Authentication Based on Score Normalization TechniqueIntelligent Informatics10.1007/978-3-642-32063-7_45(425-434)Online publication date: 2013
  • (2013)Facial Soft Biometrics for Person RecognitionSignal and Image Processing for Biometrics10.1002/9781118561911.ch3(39-55)Online publication date: 21-Jan-2013
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