Joint weighted dictionary learning and classifier training for robust biometric recognition

Rahman Khorsandi, Ali Taalimi, Mohamed Abdel-Mottaleb, Hairong Qi

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

2 Scopus citations

Abstract

In this paper, we present an automated system for robust biometric recognition based upon sparse representation and dictionary learning. In sparse representation, extracted features from the training data are used to develop a dictionary. Training data of real world applications are likely to be exposed to geometric transformations, which is a big challenge for designing of discriminative dictionaries. Classification is achieved by representing the extracted features of the test data as a linear combination of entries in the dictionary. We propose joint weighted dictionary learning and classifier training (JWDL-CT) approach which simultaneously learns from a set of training samples along with weight vectors that correspond to the atoms in the learnt dictionary. The components of the weight vector associated with an atom represent the relationship between the atom and each of the classes. The weight vectors and atoms are jointly obtained during the dictionary learning. In the proposed method, a constraint is imposed on the correlation between the atoms to decrease the similarity between these atoms. The proposed dictionary learning objective function enhances the class-discrimination capabilities of individual atoms that renders the designed dictionaries especially suitable for classification of query images with very sparse representation. Experiments conducted on the West Virginia University (WVU) and the University of Notre Dame (UND) datasets for ear recognition show that the proposed method outperforms other state-of-the-art classifiers.

Original languageEnglish (US)
Title of host publication2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1307-1311
Number of pages5
ISBN (Electronic)9781479975914
DOIs
StatePublished - Feb 23 2016
EventIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015 - Orlando, United States
Duration: Dec 13 2015Dec 16 2015

Publication series

Name2015 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2015

Other

OtherIEEE Global Conference on Signal and Information Processing, GlobalSIP 2015
CountryUnited States
CityOrlando
Period12/13/1512/16/15

Keywords

  • Biometrics
  • Classification
  • Dictionary Learning
  • Sparse Representation

ASJC Scopus subject areas

  • Information Systems
  • Signal Processing

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