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A Deep Learning Approach with Data Augmentation to Recognize Facial Expressions in Real Time
Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
Department of Computer Science and Engineering, University of Liberal Arts Bangladesh, Dhaka, Bangladesh.
Department of Computer Science and Engineering, University of Chittagong, Chittagong, Bangladesh.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-3090-7645
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2022 (English)In: Proceedings of the Third International Conference on Trends in Computational and Cognitive Engineering: TCCE 2021 / [ed] M. Shamim Kaiser; Kanad Ray; Anirban Bandyopadhyay; Kavikumar Jacob; Kek Sie Long, Springer Nature, 2022, p. 487-500Conference paper, Published paper (Refereed)
Abstract [en]

The enormous use of facial expression recognition in various sectors of computer science elevates the interest of researchers to research this topic. Computer vision coupled with deep learning approach formulates a way to solve several real-world problems. For instance, in robotics, to carry out as well as to strengthen the communication between expert systems and human or even between expert agents, it is one of the requirements to analyze information from visual content. Facial expression recognition is one of the trending topics in the area of computer vision. In our previous work, a facial expression recognition system is delivered which can classify an image into seven universal facial expressions—angry, disgust, fear, happy, neutral, sad, and surprise. This is the extension of our previous research in which a real-time facial expression recognition system is proposed that can recognize a total of ten facial expressions including the previous seven facial expressions and additional three facial expressions—mockery, think, and wink from video streaming data. After model training, the proposed model has been able to gain high validation accuracy on a combined facial expression dataset. Moreover, the real-time validation of the proposed model is also promising.

Place, publisher, year, edition, pages
Springer Nature, 2022. p. 487-500
Series
Lecture Notes in Networks and Systems, ISSN 2367-3370, E-ISSN 2367-3389 ; 348
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-89921DOI: 10.1007/978-981-16-7597-3_40Scopus ID: 2-s2.0-85126247773OAI: oai:DiVA.org:ltu-89921DiVA, id: diva2:1647487
Conference
3rd International Conference on Trends in Cognitive Computation Engineering (TCCE 2021), Johor, Malaysia, October 21-22, 2021
Note

ISBN för värdpublikation: 978-981-16-7596-6, 978-981-16-7597-3

Available from: 2022-03-28 Created: 2022-03-28 Last updated: 2023-09-05Bibliographically approved

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