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A Multi-Model Framework for Grading of Human Emotion Using CNN and Computer Vision

A Multi-Model Framework for Grading of Human Emotion Using CNN and Computer Vision

Praveen Kulkarni, Rajesh T. M.
Copyright: © 2022 |Volume: 12 |Issue: 1 |Pages: 21
ISSN: 2155-6997|EISSN: 2155-6989|EISBN13: 9781683182122|DOI: 10.4018/IJCVIP.2022010102
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MLA

Kulkarni, Praveen, and Rajesh T. M. "A Multi-Model Framework for Grading of Human Emotion Using CNN and Computer Vision." IJCVIP vol.12, no.1 2022: pp.1-21. http://doi.org/10.4018/IJCVIP.2022010102

APA

Kulkarni, P. & Rajesh T. M. (2022). A Multi-Model Framework for Grading of Human Emotion Using CNN and Computer Vision. International Journal of Computer Vision and Image Processing (IJCVIP), 12(1), 1-21. http://doi.org/10.4018/IJCVIP.2022010102

Chicago

Kulkarni, Praveen, and Rajesh T. M. "A Multi-Model Framework for Grading of Human Emotion Using CNN and Computer Vision," International Journal of Computer Vision and Image Processing (IJCVIP) 12, no.1: 1-21. http://doi.org/10.4018/IJCVIP.2022010102

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Abstract

Emotion analysis is an area which is been widely used in the forensic crime detection domain, a mentoring device for depressed students, psychologically affected patient treatment. The current system helps only in identifying the emotions but not in identifying the level of emotions like whether the individual is truly happy/sad or pretending to be happy /sad. In this proposed work a novel methodology has been introduced. We have rebuilt the Traditional Local Binary Pattern (LBP) feature operator to image the expression and combine the abstract characteristics of facial expression learned from the neural network of deep convolution with the modified features of the texture of the LBP facial expression in the full connection layer. These extracted features have been subjected as input for CNN Alex Net to classify the level of emotions. The results obtained in this phase are used in the confusion matrix for analysis of grading of emotions like Grade-1, Grade-2, and Grade-3 obtained an accuracy of 87.58% in the comparative analysis.