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An Experimental Investigation into the Performance Characteristics of TiO2/Ethylene Glycol Nanofluid Flowing in a Horizontal Counterflow Heat Exchanger Read chapter Read first chapter

2022 | OriginalPaper | Chapter

8. Recognition of Facial Expressions Based on Detection of Facial Components and HOG Characteristics

Authors : K. A. Anu, N. Ali Akbar

Published in: Intelligent Manufacturing and Energy Sustainability

Publisher: Springer Singapore

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Abstract

People were able to convey intentions and feelings through non-verbal languages such as gestures and facial expressions. However, recognizing facial expressions is a very difficult task. Numerous factors such as light, posture, and distortion can cause complications. The proposed system is a good method for facial emotion detection problems. The system, which considers the elements of the face, will help to predict emotions from an image. Then histogram of oriented gradient (HOG) is used to encode these facial elements as features. A linear support vector machine is then used to identify facial expressions. The final results of this experiment show the accuracy of our prediction.

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Literature
1.
Kwong, J.C.T., Felan Carlo, C., Garcia, P.A., Abu, R., Reyes, R.S.J.: Emotion recognition via facial expression: utilization of numerous feature descriptors in different machine learning algorithms. In: TENCON 2018–2018 IEEE Region 10 Conference, pp. 2045–2049. IEEE (2018)
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Jang, J., Cho, H., Kim, J., Lee, J., Yang, S.: Facial attribute recognition by recurrent learning with visual fixation. IEEE Transactions on Cybernetics, vol. 49, no. 2, pp. 616–625 (2018)
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Pukhrambam, M., Das, A., Saha, A.: Facial components extraction and expression recognition in static images. In: 2015 International Conference on Green Computing and Internet of Things (ICGCIoT), pp. 726–731. IEEE (2015)
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Salunke, V.V., Patil, C.G.: A new approach for automatic face emotion recognition and classification based on deep networks. In: 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), pp. 1–5. IEEE (2017)
5.
Xie, S., Hu, H.: Facial expression recognition using hierarchical features with deep comprehensive multipatches aggregation convolutional neural networks. IEEE Transactions on Multimedia, vol. 21, no. 1, pp. 211–220 (2018)
Metadata
Title
Recognition of Facial Expressions Based on Detection of Facial Components and HOG Characteristics
Authors
K. A. Anu
N. Ali Akbar
Copyright Year
2022
Publisher
Springer Singapore
Book
Intelligent Manufacturing and Energy Sustainability

Print ISBN: 978-981-16-6481-6

Electronic ISBN: 978-981-16-6482-3

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-981-16-6482-3_8

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