Large-scale nonlinear facial image classification based on approximate kernel Extreme Learning Machine

Alexandros Iosifidis, Anastasios Tefas, Ioannis Pitas

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

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Abstract

In this paper, we propose a scheme that can be used in largescale nonlinear facial image classification problems. An approximate solution of the kernel Extreme Learning Machine classifier is formulated and evaluated. Experiments on two
publicly available facial image datasets using two popular facial image representations illustrate the effectiveness and efficiency of the proposed approach. The proposed Approximate Kernel Extreme Learning Machine classifier is able to scale well in both time and memory, while achieving good generalization performance. Specifically, it is shown that it outperforms the standard ELM approach for the same time and memory requirements. Compared to the original kernel ELM approach, it achieves similar (or better) performance,
while scaling well in both time and memory with respect to the training set cardinality.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing (ICIP 2015)
Subtitle of host publicationProceedings of a meeting held 27-30 September 2015, Quebec City, Quebec, Canada
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages2449-2453
Number of pages5
ISBN (Electronic)9781479983391
ISBN (Print)9781479983407
DOIs
Publication statusPublished - Jan 2016
Event2015 IEEE International Conference on Image Processing (ICIP) - Quebec City, ON, Canada
Duration: 27 Sep 201530 Sep 2015

Conference

Conference2015 IEEE International Conference on Image Processing (ICIP)
CountryCanada
CityQuebec City, ON
Period27/09/1530/09/15

Keywords

  • Nonlinear Facial Image Classification
  • Extreme Learning Machine
  • Approximate Methods

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