Enhanced discriminant linear regression classification for face recognition

Xiaochao Qu, Hyong Joong Kim

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

3 Citations (Scopus)

Abstract

Linear Discriminant regression classification (L-DRC) embeds the fisher criterion into the linear regression classification (LRC) and can achieve more robust classification performance for face recognition. In this paper, we propose an enhanced discriminant linear regression classification (EDLRC) algorithm to further improve the discriminant power of LDRC. When calculating the between-class reconstruction error (BCRE), only those classes that are more easily to be misclassified into are considered. After maximizing the ratio of BCRE and within-class reconstruction error (WCRE), the obtained projection matrix in EDLRC is more effective than the projection matrix in LDRC, which is verified by extensive experiments.

Original languageEnglish
Title of host publicationIEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings
PublisherIEEE Computer Society
ISBN (Print)9781479928439
DOIs
Publication statusPublished - 2014 Jan 1
Event9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014 - Singapore, Singapore
Duration: 2014 Apr 212014 Apr 24

Other

Other9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing, IEEE ISSNIP 2014
CountrySingapore
CitySingapore
Period14/4/2114/4/24

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

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  • Cite this

    Qu, X., & Kim, H. J. (2014). Enhanced discriminant linear regression classification for face recognition. In IEEE ISSNIP 2014 - 2014 IEEE 9th International Conference on Intelligent Sensors, Sensor Networks and Information Processing, Conference Proceedings [6827696] IEEE Computer Society. https://doi.org/10.1109/ISSNIP.2014.6827696