Intelligent facial emotion recognition using moth-firefly optimization

Zhang, Li, Mistry, Kamlesh, Neoh, Siew Chin and Lim, Chee Peng 2016, Intelligent facial emotion recognition using moth-firefly optimization, Knowledge-based systems, vol. 111, pp. 248-267, doi: 10.1016/j.knosys.2016.08.018.

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Title Intelligent facial emotion recognition using moth-firefly optimization
Author(s) Zhang, Li
Mistry, Kamlesh
Neoh, Siew Chin
Lim, Chee PengORCID iD for Lim, Chee Peng
Journal name Knowledge-based systems
Volume number 111
Start page 248
End page 267
Total pages 20
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-11-01
ISSN 0950-7051
Keyword(s) Facial expression recognition
Feature selection
Evolutionary algorithm
Ensemble classifier
Summary In this research, we propose a facial expression recognition system with a variant of evolutionary firefly algorithm for feature optimization. First of all, a modified Local Binary Pattern descriptor is proposed to produce an initial discriminative face representation. A variant of the firefly algorithm is proposed to perform feature optimization. The proposed evolutionary firefly algorithm exploits the spiral search behaviour of moths and attractiveness search actions of fireflies to mitigate premature convergence of the Levy-flight firefly algorithm (LFA) and the moth-flame optimization (MFO) algorithm. Specifically, it employs the logarithmic spiral search capability of the moths to increase local exploitation of the fireflies, whereas in comparison with the flames in MFO, the fireflies not only represent the best solutions identified by the moths but also act as the search agents guided by the attractiveness function to increase global exploration. Simulated Annealing embedded with Levy flights is also used to increase exploitation of the most promising solution. Diverse single and ensemble classifiers are implemented for the recognition of seven expressions. Evaluated with frontal-view images extracted from CK+, JAFFE, and MMI, and 45-degree multi-view and 90-degree side-view images from BU-3DFE and MMI, respectively, our system achieves a superior performance, and outperforms other state-of-the-art feature optimization methods and related facial expression recognition models by a significant margin.
Language eng
DOI 10.1016/j.knosys.2016.08.018
Field of Research 099999 Engineering not elsewhere classified
08 Information And Computing Sciences
15 Commerce, Management, Tourism And Services
17 Psychology And Cognitive Sciences
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2016, The Authors
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Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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