A Novel Architecture combining convolutional neural network and support vector machine for expression recognition in driving environment
- Conference
- Location
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Wentworth Institute of Technology, Massachusetts
- Publication Date
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April 22, 2022
- Start Date
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April 22, 2022
- End Date
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April 23, 2022
- Page Count
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3
- DOI
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10.18260/1-2--42150
- Permanent URL
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https://peer.asee.org/42150
- Download Count
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392
Paper Authors
SUPARSHYA BABU SUKHAVASI
Khaled Elleithy University of Bridgeport
Dr. Elleithy is the Dean of College of Engineering, Business and Education at the University of Bridgeport. He is a distinguished professor of Computer Science and Engineering. His research interests includes wireless sensor networks, mobile communications, network security, quantum computing, and formal approaches for design and verification. He has published more than three hundred fifty research papers in national / international journals and conferences in his areas of expertise. Dr. Elleithy is the editor or co-editor for 12 books published by Springer.
Dr. Elleithy received the B.Sc. degree in computer science and automatic control from Alexandria University in 1983, the MS Degree in computer networks from the same university in 1986, and the MS and Ph.D. degrees in computer science from The Center for Advanced Computer Studies at the University of Louisiana - Lafayette in 1988 and 1990, respectively.
Prof. Elleithy has more than 30 years of teaching experience. His teaching evaluations were distinguished in all the universities he joined. He is the recipient of the "Distinguished Professor of the Year", University of Bridgeport, academic year 2006-2007. He supervised hundreds of senior projects, MS theses and Ph.D. dissertations. He developed and introduced many new undergraduate/graduate courses. He also developed new teaching / research laboratories in his area of expertise. His students have won more than twenty prestigious national / international awards from IEEE, ACM, and ASEE.
Dr. Elleithy is a member of the technical program committees of many international conferences as recognition of his research qualifications. He served as a guest editor for several international journals. He was the chairperson of the International Conference on Industrial Electronics, Technology & Automation. Furthermore, he is the co-Chair and co-founder of the Annual International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering virtual conferences 2005 - 2014.
Dr. Elleithy is a member of several technical and honorary societies. He is a Senior Member of the IEEE computer society. He is a member of the Association of Computing Machinery (ACM) since 1990, member of ACM SIGARCH (Special Interest Group on Computer Architecture) since 1990, member of the honor society of Phi Kappa Phi University of South Western Louisiana Chapter since April 1989, member of IEEE Circuits & Systems society since 1988, member of the IEEE Computer Society since 1988, and a lifetime member of the Egyptian Engineering Syndicate since June 1983.
Abdelrahman Elleithy William Paterson University
Dr. Elleithy is an Assistant Professor in the Department of Computer Science at William Paterson University, Wayne, New Jersey since September 2017. His research interests include wireless sensor networks, mobile communications and network security. He has published many research papers in international journals and conferences in his area of expertise.
Dr. Elleithy has worked as a Visiting Assistant Professor of Computer Science at Texas A&M University between August 2014 to August 2016 and as a lecturer of the same department from September 2016 to June 2017.
Dr. Elleithy received the BS, MS, and Ph.D. degrees in computer science from the University of Bridgeport in 2007, 2008 and 2013 respectively.
Dr. Elleithy is a member of the technical program committees of many international conferences. He served as a member of the technical program committee of the Annual International Joint Conferences on Computer, Information, and Systems Sciences, and Engineering virtual conferences 2010 – 2014 and the technical program committee of 2016 Annual IEEE Connecticut Conference on Industrial Electronics, Technology & Automation.
Dr. Elleithy is a member of several technical and honorary societies. He is a Member of IEEE, Association of Computing Machinery (ACM), and the honor society of UPE.
Ahmed El-Sayed University of Bridgeport
Dr. El-Sayed received the B.Sc. degree in electrical engineering from the Department of Electrical Engineering, Alexandria University, Egypt, in 2003, the M.Sc. degree in engineering mathematics from the Department of Engineering Mathematics and Physics, Alexandria University, in 2006, and the M.Sc. and Ph.D. degrees in computer engineering from the Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT, USA, in 2011 and 2016, respectively. Currently, he is Assistant Professor of Electrical and Computer Engineering at the University of Bridgeport. He published articles in the fields of robotics, soft computing, and computer vision. His research interests include robotics, AI, fuzzy systems, soft computing, machine learning, pattern recognition, and computer vision. He is a member of IEEE, ASEE, the Honor Society of Phi Kappa Phi, the Honor Society for Computing and Information Disciplines Upsilon Pi Epsilon (UPE), and a lifetime member of the Egyptian Engineering Syndicate.
SUSRUTHABABU SUKHAVASI University of Bridgeport
Abstract
Emotion monitoring is one of the key parameters in our day-to-day lives. The person's state of mind can be readable from their emotions. Expressing emotions usually happens in two ways of communication, namely verbally and non-verbally. Verbal communication is easy to communicate and understand between people in most situations, whereas nonverbal communication, like showing emotions, is difficult to understand in some cases. These mental emotions can control the person to go through either good situations or bad situations, especially if the emotions of the driver are considered, the good emotions like happiness, neutral can make the driver be in a good mental state can drive the vehicle safely however emotions like sad, angry, disgust, afraid are the emotions that influence the driver capabilities can cause accidents. To avoid this, advanced driver assistance systems are introduced in automotive vehicles to assist the driver for various functions for safety purposes. In addition to that, Emotion monitoring in advanced driver assistance systems can be accomplished by using facial expression recognition technology, which is evolved by training a convolutional neural network with applying machine learning and deep learning approaches to detect faces and predict emotions from the feature obtained from the networks. To achieve this, we proposed a novel architecture combining a convolutional neural network and support vector machine for expression recognition in the driving environment. The experimental results demonstrate the effectiveness of the proposed architecture by achieving remarkable accuracy.
SUKHAVASI, S. B., & Elleithy, K., & Elleithy, A., & El-Sayed, A., & SUKHAVASI, S. (2022, April), A Novel Architecture combining convolutional neural network and support vector machine for expression recognition in driving environment Paper presented at ASEE-NE 2022, Wentworth Institute of Technology, Massachusetts. 10.18260/1-2--42150
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