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Intelligent facial emotion recognition using moth-firefly optimization

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Version 2 2024-06-06, 08:07
Version 1 2016-11-30, 15:18
journal contribution
posted on 2024-06-06, 08:07 authored by L Zhang, K Mistry, SC Neoh, Chee Peng Lim
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.

History

Journal

Knowledge-based systems

Volume

111

Pagination

248-267

Location

Amsterdam, The Netherlands

Open access

  • Yes

ISSN

0950-7051

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2016, The Authors

Publisher

Elsevier