Hybrid CNN-SVM Classifier Framework for Driver Emotion Detection System
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Authors
Sukhavasi, Suparshya Babu
Sukhavasi, Susrutha Babu
Elleithy, Khaled
El-Sayed, Ahmed
Elleithy, Abdelrahman
Issue Date
2022-03-27
Type
Other
Language
en_US
Keywords
Deep neural networks , Advanced driver assistance systems (ADAS) , Face detection , Facial expression recognition , Driver emotion detection , DeepNet , Machine learning
Alternative Title
Abstract
Many studies have proved that the driver’s emotions are the significant factors that manage the driver’s behavior, leading to severe vehicle collisions. The ADAS systems can assist various functions for proper driving and estimate drivers’ capability of stable driving behavior and road safety. 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. 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.