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Emotion-Based Human-Computer Interaction

Emotion-Based Human-Computer Interaction

Sujigarasharma K. (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India), Rathi R. (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India), Visvanathan P. (School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India), and Kanchana R. (Computer Science and Engineering, Vel Tech Ranganathan Dr.Sagunthala R&D Institute of Science and Technology, Chennai, India)
Copyright: © 2023 |Pages: 15
DOI: 10.4018/978-1-6684-5673-6.ch009
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Abstract

One of the important aspects of human-computer interaction is the detection of emotions using facial expressions. Emotion recognition has problems such as facial expressions, variations of posture, non-uniform illuminations, and so on. Deep learning techniques becomes important to solve these classification problems. In this chapter, VGG19, Inception V3, and Resnet50 pre-trained networks are used for the transfer learning approach to predict human emotions. Finally, the study achieved 98.32% of accuracy for emotion recognition and classification using the CK+ dataset.
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The authors (Ozdemir et al., 2019) suggested a LeNet architecture-based facial detection system. This study makes use of a combined KDEF and JAFFE dataset The Haar cascade package is being used to filter the emotion recognition. This task was accomplished with an accuracy of 95.40%.

The authors (Jyostna & Veeranjaneyulu, 2019) demonstrated how to deal with different situations using a CNN. VGG16 and SVM classifier is deployed for extracting features. The algorithm had an 82.27% of accuracy without face detection and 87.16% of accuracy with face detection on the CK+ database. The author (Fan et al., 2018) presented recognising emotional expressions for the multi-region CNN method, as indicated in this paper. The sub-networks provided the attributes derived from the eyes, mouth and nose. To estimate emotions, the ratings over the sub-networks are integrated.

In this article (Wang et al., 2019) collect the most number of data. Here they used FER2013, CK +, JAFFE and SFEW datasets to test the model. The databases RAF- DB and AFEW 7.0 have been used in this study. The authors (Sreelakshmi & Sumithra, 2019) created an emotion identification system based on the MobileNet V2 architecture. The model is evaluated on real-time images and obtains an accuracy of 90.15%. Resnet50 and VGG16 facial expression recognition were exhibited as the state of the science.

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