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ReserarchID
CSTGVAP1X7
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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Emotion Recognition is an important area of work to improve the interaction between human and machine. Complexity of emotion makes the acquisition task more difficult. Quondam works are proposed to capture emotion through unimodal mechanism such as only facial expressions or only vocal input. More recently, inception to the idea of multimodal emotion recognition has increased the accuracy rate of the detection of the machine. Moreover, deep learning technique with neural network extended the success ratio of machine in respect of emotion recognition. Recent works with deep learning technique has been performed with different kinds of input of human behavior such as audio-visual inputs, facial expressions, body gestures, EEG signal and related brainwaves. Still many aspects in this area to work on to improve and make a robust system will detect and classify emotions more accurately. In this paper, we tried to explore the relevant significant works, their techniques, and the effectiveness of the methods and the scope of the improvement of the results.
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