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The subjectivity and variability exist in the human emotion perception differs from person to person. In this work, we propose a framework that models the majority of emotion annotation integrated with modeling of subjectivity in improving emotion categorization performances. Our method achieves a promising accuracy of 61.48% on a four-class emotion recognition task. To the best of our knowledge, while there are many works in studying annotator subjectivity, this is one of the first works that have explicitly modeled jointly the consensus with individuality in emotion perception to demonstrate its improvement in classifying emotion in a benchmark corpus.

In our immediate future work, we will evaluate the proposed framework on other public large-scaled emotional database with multiple annotators, e.g., NNIME, to further justify its robustness. We also plan to extend our framework to includ other behavior attributes, e.g., lexical content and body movements. Furthermore, the subjective nature of emotion perception has been shown to be related to the rater personality, a joint modeling of rater’s characteristics with his/her subjectivity in emotion perception may lead to further advancement in robust emotion recognition.