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Machine Learning Techniques for Biometrics

  • Chapter
Handbook of Remote Biometrics

Part of the book series: Advances in Pattern Recognition ((ACVPR))

Abstract

This chapter reports recent advances in the statistical learning literature that may be of interest for biometrics. In particular we discuss two different algorithmic settings, binary classification and multi-task learning, and analyze the two closely related problems of feature selection and feature learning. In the binary case the theoretical and algorithmic advances to feature selection are applied to solve face detection and face authentication problems. In the multi-task case we show how the data structure described by a group of features common to the various tasks can be effectively learned, and then we discuss how this approach could be used to address face recognition.

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Author information

Authors and Affiliations

  1. DISI Università degli Studi di Genova, Via Dodecaneso 35, 16146 Genova, Italy

    Francesca Odone

  2. Department of Computer Science, University College London Malet Place London WC1E 6BT, UK

    Massimiliano Pontil

  3. DISI Università degli Studi di Genova, Via Dodecaneso 35, 16146 Genova, Italy

    Alessandro Verri

Authors
  1. Francesca Odone
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  2. Massimiliano Pontil
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  3. Alessandro Verri
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Corresponding author

Correspondence to Francesca Odone .

Editor information

Editors and Affiliations

  1. Universitá of Sassari, Dipto. Architettura e Pianificazione, Piazza Duomo, 6, 07041 Alghero SS, Italy

    Massimo Tistarelli (Prof.) (Prof.)

  2. Chinese Academy of Science Institute of Automation, Center Biometrics Research & Security, Room 1227, Zhongguancun 95, 100080 Beijing, China

    Stan Z. Li

  3. University of Maryland, Center for Automation Research, College Park, MD 20742-3275, USA

    Rama Chellappa

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Odone, F., Pontil, M., Verri, A. (2009). Machine Learning Techniques for Biometrics. In: Tistarelli, M., Li, S.Z., Chellappa, R. (eds) Handbook of Remote Biometrics. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84882-385-3_10

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  • DOI: https://doi.org/10.1007/978-1-84882-385-3_10

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-384-6

  • Online ISBN: 978-1-84882-385-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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