Journal of Advances in Engineering Sciences

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Recognize Human Emotions: Recognition from DCT


Affiliations
1 Computer Engineering Department, Sinhgad Institute of Technology, Lonavala, Affiliation with Pune University, Maharashtra, India
2 Information Technology Department, Sinhgad Institute of Technology, Lonavala, Affiliation with Pune University, Maharashtra, India
     

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The ability to recognize emotion is one of the hallmarks of emotional intelligence, an aspect of human intelligence that has been argued to be even more important than mathematical and verbal intelligence. This paper proposes that machine intelligence needs to include emotional intelligence and demonstrate results toward this goal: developing a machines ability to recognize human emotion from given facial parameter. We describe difficult issues unique to obtain reliable affective data and collect a large set of data from a subject and experience each of two emotional states. This paper presents techniniqus for feature extraction and use algorithm for classification, which is on kernel based. We got 100% recognition accuracy on three classes of emotion, including neutral.

Keywords

DCT, Mean, Entropy, Energy, SVM.
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  • Recognize Human Emotions: Recognition from DCT

Abstract Views: 176  |  PDF Views: 3

Authors

S. B. Nimbekar
Computer Engineering Department, Sinhgad Institute of Technology, Lonavala, Affiliation with Pune University, Maharashtra, India
D. D. Badgujar
Information Technology Department, Sinhgad Institute of Technology, Lonavala, Affiliation with Pune University, Maharashtra, India

Abstract


The ability to recognize emotion is one of the hallmarks of emotional intelligence, an aspect of human intelligence that has been argued to be even more important than mathematical and verbal intelligence. This paper proposes that machine intelligence needs to include emotional intelligence and demonstrate results toward this goal: developing a machines ability to recognize human emotion from given facial parameter. We describe difficult issues unique to obtain reliable affective data and collect a large set of data from a subject and experience each of two emotional states. This paper presents techniniqus for feature extraction and use algorithm for classification, which is on kernel based. We got 100% recognition accuracy on three classes of emotion, including neutral.

Keywords


DCT, Mean, Entropy, Energy, SVM.