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Human Emotion Recognition from Facial Expressions

Ortil Msugh *, Twaki Koko Grace**

*-**Department of Computer Science. FCT College of Education, Abuja, Nigeria.

Periodicity:December - February'2019
DOI : https://doi.org/10.26634/jpr.5.4.15539

Abstract

Emotion recognition remains a potential research area as efforts to make machines to mimic humans in most areas of human life is yet actualized. This paper presents an emotion recognition system, to enhance recognition accuracy for better user experience. Principal Component Analysis (PCA) was implemented via Singular Value Decomposition (SVD) and used for feature extraction process. In classification process, Discrete Hidden Markov Model (HMM) was utilized in a principled manner. Two-dimensional spatial face features were realized by varying quantization levels. The quantization level with the efficient feature description, judged by the highest recognition accuracy was chosen to train the system. The recognition accuracy of the system was studied on two publicly available datasets, namely, JAFFE and Cohn Kanade (CK) datasets. The system showed better performances compared with other state of the art systems.

Keywords

Facial Expressions, Emotion Recognition, PCA, HMM.

How to Cite this Article?

Msugh, O., & Grace,T. K. (2019). Human Emotion Recognition from Facial Expressions. i-manager’s Journal on Pattern Recognition, 5(4), 1-12. https://doi.org/10.26634/jpr.5.4.15539

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