Ensemble feature selection in face recognition: ICMLA 2012 challenge

Salem Alelyani, Huan Liu

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

5 Scopus citations

Abstract

Ensemble feature selection is known for its robustness and generalization of highly accurate predictive models. In this paper, we use different filter-based feature selection methods in an ensemble manner to improve face recognition. The goal is to distinguish human faces from avatar faces. Our approach was able to achieve very high accuracy, 99%, using less than 1% of the pixels in each image. This was obtained after removing irrelevant features which is known to degrade learning performance and model stability.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Pages588-591
Number of pages4
DOIs
StatePublished - Dec 1 2012
Event11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012 - Boca Raton, FL, United States
Duration: Dec 12 2012Dec 15 2012

Publication series

NameProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Volume2

Other

Other11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
CountryUnited States
CityBoca Raton, FL
Period12/12/1212/15/12

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ASJC Scopus subject areas

  • Human-Computer Interaction
  • Education

Cite this

Alelyani, S., & Liu, H. (2012). Ensemble feature selection in face recognition: ICMLA 2012 challenge. In Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012 (pp. 588-591). [6406801] (Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012; Vol. 2). https://doi.org/10.1109/ICMLA.2012.182