Sparse representation and least squares-based classification in face recognition

Michael Iliadis, Leonidas Spinoulas, Albert S. Berahas, Haohong Wang, Aggelos K Katsaggelos

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

6 Scopus citations

Abstract

In this paper we present a novel approach to face recognition. We propose an adaptation and extension to the state-of-the-art methods in face recognition, such as sparse representation-based classification and its extensions. Effectively, our method combines the sparsity-based approaches with additional least-squares steps and exhitbits robustness to outliers achieving significant performance improvement with little additional cost. This approach also mitigates the need for a large number of training images since it proves robust to varying number of training samples.

Original languageEnglish (US)
Title of host publication2014 Proceedings of the 22nd European Signal Processing Conference, EUSIPCO 2014
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages526-530
Number of pages5
ISBN (Electronic)9780992862619
StatePublished - Jan 1 2014
Event22nd European Signal Processing Conference, EUSIPCO 2014 - Lisbon, Portugal
Duration: Sep 1 2014Sep 5 2014

Other

Other22nd European Signal Processing Conference, EUSIPCO 2014
CountryPortugal
CityLisbon
Period9/1/149/5/14

Keywords

  • Face recognition
  • classification
  • sparse representation

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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