• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Multi-classifier Fusion Based Facial Expression Recognition Approach

Vol. 8, No.1, January 29, 2014
10.3837/tiis.2014.01.012, Download Paper (Free):

Abstract

Facial expression recognition is an important part in emotional interaction between human and machine. This paper proposes a facial expression recognition approach based on multi-classifier fusion with stacking algorithm. The kappa-error diagram is employed in base-level classifiers selection, which gains insights about which individual classifier has the better recognition performance and how diverse among them to help improve the recognition accuracy rate by fusing the complementary functions. In order to avoid the influence of the chance factor caused by guessing in algorithm evaluation and get more reliable awareness of algorithm performance, kappa and informedness besides accuracy are utilized as measure criteria in the comparison experiments. To verify the effectiveness of our approach, two public databases are used in the experiments. The experiment results show that compared with individual classifier and two other typical ensemble methods, our proposed stacked ensemble system does recognize facial expression more accurately with less standard deviation. It overcomes the individual classifier’s bias and achieves more reliable recognition results.


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Cite this article

[IEEE Style]
Xibin Jia, Yanhua Zhang, David Powers and Humayra Binte Ali, "Multi-classifier Fusion Based Facial Expression Recognition Approach," KSII Transactions on Internet and Information Systems, vol. 8, no. 1, pp. 196-212, 2014. DOI: 10.3837/tiis.2014.01.012

[ACM Style]
Jia, X., Zhang, Y., Powers, D., and Ali, H. B. 2014. Multi-classifier Fusion Based Facial Expression Recognition Approach. KSII Transactions on Internet and Information Systems, 8, 1, (2014), 196-212. DOI: 10.3837/tiis.2014.01.012