Oluyinka Iyabo Omotosho
ABSTRACT
The ability to understand facial expressions is an important part of nonverbal communication. The value in understanding facial expressions is to gather information about how the other person is feeling and guide our interaction accordingly. A person's ability to interpret emotions is very important for Effective communication. Recent researches show that emotional states and motivation directly or indirectly influences of student's learning process. This work is however a plunge into how systems can correctly detect recognize and classify human (Students) facial emotional expression through various image sensors, using Convolutional Neural Network (CNN). Dataset containing 28821 Face images were acquired. All images were used for training and testing using Convolutional Neural Network algorithm implemented in MATLAB software. 80% of the image dataset were used in training the system, while 20% were used for testing the system. The Trained CNN classifier classify image emotions using the Adam optimizer for higher accuracy.
- Fredrickson B. (1998). What good are positive emotions? Review of general psychology, 2(3), 300-319.Google Scholar
- Theonas G, Hobbs D, and Rigas D. (2007). The Effect of Facial Expressions on Students in Virtual Educational Environments World Academy of Science, Engineering and Technology International Journal of Educational and Pedagogical Sciences 1(11), PP 626-632.Google Scholar
- Tai-Hoon Kim (2010). Conference: Information Security and Assurance - 4th International Conference, ISA 2010, Miyazaki, Japan, June 23-25, 2010. Proceedings.Google Scholar
- Zhang Wei (1988). "Shift-invariant pattern recognition neural network and its optical architecture". Proceedings of Annual Conference of the Japan Society of Applied Physics.Google Scholar
- Anwar Hossain and Shahriar Alam Sajib (2019) Classification of Image using Convolutional Neural Network (CNN). Global Journal of Computer Science and Technology: Intelligence 19(2).Online ISSN: 0975-4172 & Print ISSN: 0975-4350.Google Scholar
- Yamashita R., Nishio M and Do, K. (2018). Convolutional neural networks: an over reiew and application in radiology. Insights Imaging 9, 611–629. https://doi.org/10.1007/s13244-018-0639-9Google Scholar
- CSpalter A, LeGrand M., Taichi S and Simpson R. (2000). Considering a Full Range of Teaching Techniques for Use in Interactive Educational Software: A Practical Guide and Brainstorming Session, in Proceedings of IEEE Frontiers in Education.Google ScholarCross Ref
- Jelfs, A.and Colbourn, C. (2002) “Virtual Seminars and their Impact on the Role of the Teaching Staff”, Computers in Education, 38, 127-136.Google ScholarDigital Library
- Clements, M. (2001), Using Guests in the Virtual Classroom, 9th Annual Learning & Teaching Conference, Nottingham,Google Scholar
- Barker B.and Dickson M. (1996). Distance learning technologies in K-12 Schools: past, present and future practice, in Tech Trends, 41(6), pp 19-22.Google ScholarCross Ref
- Palloff R. and Pratt K. (2001), Lessons for the cyberspace classroom: the realities of online teaching, San Francisco: Jossey-Bass.Google Scholar
- King F., Young M., Drivere-Richmond K. and Schrader P. (2001). Defining distance learning and distance education, in Educational Technology Review, 9(1).Google Scholar
- Moore M. (1987), University distance education of adults, in Tech Trends, 32(4), pp 13-18.Google ScholarCross Ref
- Russell G. and Holkner B. (2000), Virtual Schools, in Futures, 32 (9-10), pp 887-897.Google ScholarCross Ref
Index Terms
- A Facial Emotion Detection and Classification System using Convoluted Neural Networks
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