Multitask multivariate common sparse representations for robust multimodal biometrics recognition

Heng Zhang, Vishal M. Patel, Rama Chellappa

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

In this paper, we propose multitask multivairate common sparse representations for robust multimodal biometrics recognition. The proposed approach can be viewed as an extension of previous work on joint sparse representations for robust multimodal biometrics recognition. The proposed algorithm utilizes the discriminative information among different modalities simultaneously by enforcing the common sparse representation across all the modalities and achieves more robust multimodal recognition especially when all modalities are noisy and 'weak'. Alternating direction method of multipliers is proposed to solve the resulting optimization problem. Experiments on two biometric datasets show that our method performs better than the state-of-the-art fusion methods.

Original languageEnglish (US)
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Pages202-206
Number of pages5
ISBN (Electronic)9781479983391
DOIs
StatePublished - Dec 9 2015
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: Sep 27 2015Sep 30 2015

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2015-December
ISSN (Print)1522-4880

Other

OtherIEEE International Conference on Image Processing, ICIP 2015
Country/TerritoryCanada
CityQuebec City
Period9/27/159/30/15

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Keywords

  • common sparse representation
  • multimodal biometrics recognition

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