Person authentication using nearest feature line embedding transformation and biased discriminant analysis

Cheng Ta Hsieh, Chin Chuan Han, Chang Hsing Lee, Kuo Chin Fan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Personal authentication (PA) on smartphones plays the crucial role in mobile payment. Facial features are the most user-friendly biometric feature because of the build-in camera, when we use smartphones as the payment devices. In this study, a novel authenticated method is proposed for PA by integrating feature line embedding (FLE) transformation and biased discriminant analysis (BDA) by using facial features. Due to the few training samples, the discriminant power is limited for learning. In feature spaces, feature lines are regarded as the feature combination between two training samples and infinitely simulate the possible features of various conditions for training. In PA, only positive samples is used to calculate the within-class scatter, and the between class scatter is also calculated using negative samples by the BDA strategy. Compared with the traditional two-class classification and BDA problems, the FLE integrates with BDA to obtain a better dimension reduction transformation. A support vector machine (SVM) classifier is further trained to determine a query sample is a real or a forged sample.

Original languageEnglish
Title of host publicationProceedings - 2017 International Carnahan Conference on Security Technology, ICCST 2017
EditorsJavier Ortega-Garcia, Aythami Morales, Julian Fierrez, Ruben Vera-Rodriguez, Riccardo Lazzeretti
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538615850
DOIs
StatePublished - 5 Dec 2017
Event2017 International Carnahan Conference on Security Technology, ICCST 2017 - Madrid, Spain
Duration: 23 Oct 201726 Oct 2017

Publication series

NameProceedings - International Carnahan Conference on Security Technology
Volume2017-October
ISSN (Print)1071-6572

Conference

Conference2017 International Carnahan Conference on Security Technology, ICCST 2017
Country/TerritorySpain
CityMadrid
Period23/10/1726/10/17

Keywords

  • biased discriminant analysis
  • biometric features
  • facial features
  • feature line embedding
  • personal authentication

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