Pose Variance, Illuminations and Occlusions involved Driver Emotion Detection System
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Authors
Sukhavasi, Susrutha Babu
Sukhavasi, Suparshya 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 , K.L.T. , MTCNN , Facial expression recognition , Driver emotion detection , DeepNet , Machine learning
Alternative Title
Abstract
Monitoring the emotions of drivers are the key aspects while designing the advanced driver assistance systems (ADAS) in vehicles. To ensure the safety and track the possibility of the accidents, the emotion monitoring will play a key role in justifying the mental status of the driver. Recent developments in face expression recognition have brought the tremendous attention across the world due to its intellectual capabilities to track the facial expressions. Machine learning and deep learning technologies have helped a lot in developing an efficient face expression recognition systems. Two novel approaches using machine learning, deep learning algorithms and residual neural networks are proposed to monitor six class of expressions of the driver in different pose variations and occlusions. We obtained the better accuracies with these two novel approaches when compared to the state of art methods.