A Hybrid Model for Driver Emotion Detection Using Feature Fusion Approach

Suparshya Babu Sukhavasi, Susrutha Babu Sukhavasi, Khaled Elleithy, Ahmed El-Sayed and Abdelrahman Elleithy
Additional contact information
Suparshya Babu Sukhavasi: Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA
Susrutha Babu Sukhavasi: Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA
Khaled Elleithy: Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA
Ahmed El-Sayed: Department of Computer Science and Engineering, University of Bridgeport, Bridgeport, CT 06604, USA
Abdelrahman Elleithy: Department of Computer Science, William Paterson University, Wayne, NJ 07470, USA

IJERPH, 2022, vol. 19, issue 5, 1-19

Abstract: Machine and deep learning techniques are two branches of artificial intelligence that have proven very efficient in solving advanced human problems. The automotive industry is currently using this technology to support drivers with advanced driver assistance systems. These systems can assist various functions for proper driving and estimate drivers’ capability of stable driving behavior and road safety. Many studies have proved that the driver’s emotions are the significant factors that manage the driver’s behavior, leading to severe vehicle collisions. Therefore, continuous monitoring of drivers’ emotions can help predict their behavior to avoid accidents. A novel hybrid network architecture using a deep neural network and support vector machine has been developed to predict between six and seven driver’s emotions in different poses, occlusions, and illumination conditions to achieve this goal. To determine the emotions, a fusion of Gabor and LBP features has been utilized to find the features and been classified using a support vector machine classifier combined with a convolutional neural network. Our proposed model achieved better performance accuracy of 84.41%, 95.05%, 98.57%, and 98.64% for FER 2013, CK+, KDEF, and KMU-FED datasets, respectively.

Keywords: convolutional neural network; hybrid model; driver emotion detection; ADAS (advanced driver assistance systems); facial expression recognition; machine learning; support vector machine (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/5/3085/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/5/3085/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:5:p:3085-:d:765123

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().