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, 111. pp. 248-267. ISSN 0950-7051

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Official URL: http://dx.doi.org/10.1016/j.knosys.2016.08.018

Abstract

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.

Item Type: Article
Uncontrolled Keywords: Facial expression recognition; Feature selection; Evolutionary algorithm; Ensemble classifier
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: Becky Skoyles
Date Deposited: 10 Nov 2016 15:36
Last Modified: 13 May 2017 21:06
URI: http://nrl.northumbria.ac.uk/id/eprint/28489

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