Facial expression recognition using firefly-based feature optimization

Kamlesh Mistry, Li Zhang, Graham Sexton, Yifeng Zeng, Mengda He

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)


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.
Original languageEnglish
Title of host publication2017 IEEE Congress on Evolutionary Computation (CEC)
Place of PublicationPiscataway
Number of pages7
ISBN (Electronic)9781509046010
ISBN (Print)9781509046027
Publication statusPublished - 1 Jun 2017
EventIEEE Congress on Evolutionary Computation - San Sebastián, Spain
Duration: 5 Jun 20178 Jun 2017


ConferenceIEEE Congress on Evolutionary Computation
Abbreviated titleCEC 2017
CitySan Sebastián


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