Facial expression recognition using firefly-based feature optimization

Mistry, Kamlesh, Zhang, Li, Sexton, Graham, Zeng, Yifeng and He, Mengda (2017) Facial expression recognition using firefly-based feature optimization. In: 2017 IEEE Congress on Evolutionary Computation (CEC): Donostia, San Sebastián June 5-8, 2017. IEEE, Piscataway, NJ, pp. 1652-1658. ISBN 9781509046027, 9781509046010

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Official URL: https://doi.org/10.1109/CEC.2017.7969500


Automatic facial expression recognition plays an important role in various application domains such as medical imaging, surveillance and human-robot interaction. This research proposes a novel facial expression recognition system with modified Local Gabor Binary Patterns (LGBP) for feature extraction and a firefly algorithm (FA) variant for feature optimization. First of all, in order to deal with illumination changes, scaling differences and rotation variations, we propose an extended overlap LGBP to extract initial discriminative facial features. Then a modified FA is proposed to reduce the dimensionality of the extracted facial features. This FA variant employs Gaussian, Cauchy and Levy distributions to further mutate the best solution identified by the FA to increase exploration in the search space to avoid premature convergence. The overall system is evaluated using three facial expression databases (i.e. CK+, MMI, and JAFFE). The proposed system outperforms other heuristic search algorithms such as Genetic Algorithm and Particle Swarm Optimization and other existing state-of-the-art facial expression recognition research, significantly.

Item Type: Book Section
Uncontrolled Keywords: feature selection, facial expression recognition, firefly optimization
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Becky Skoyles
Date Deposited: 05 Jul 2018 11:06
Last Modified: 13 Aug 2021 13:17
URI: http://nrl.northumbria.ac.uk/id/eprint/34823

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