Multi-classifier fusion based facial expression recognition approach

Xi-Bin Jia, Yanhua Zhang, David Powers, Humayra Ali

    Research output: Contribution to journalArticlepeer-review

    9 Citations (Scopus)


    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.

    Original languageEnglish
    Pages (from-to)196-212
    Number of pages17
    JournalKSII Transactions on Internet and Information Systems
    Issue number1
    Publication statusPublished - 2014


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