scholarly journals A Study on Facial Expression Recognition Model Using an Adaptive Learning Capability

10.5772/13859 ◽  
2011 ◽  
Author(s):  
Masaki Ishii
Keyword(s):  
Facial Expression ◽  
Adaptive Learning ◽  
Facial Expression Recognition ◽  
Expression Recognition ◽  
Learning Capability ◽  
Recognition Model

scholarly journals DRER: Deep Learning–Based Driver’s Real Emotion Recognizer

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2166
Author(s):  
Geesung Oh ◽  
Junghwan Ryu ◽  
Euiseok Jeong ◽  
Ji Hyun Yang ◽  
Sungwook Hwang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Emotional State ◽  
Electrodermal Activity ◽  
Intelligent Vehicles ◽  
Expression Recognition ◽  
Recognition Model ◽  
Increase In Accuracy

In intelligent vehicles, it is essential to monitor the driver’s condition; however, recognizing the driver’s emotional state is one of the most challenging and important tasks. Most previous studies focused on facial expression recognition to monitor the driver’s emotional state. However, while driving, many factors are preventing the drivers from revealing the emotions on their faces. To address this problem, we propose a deep learning-based driver’s real emotion recognizer (DRER), which is a deep learning-based algorithm to recognize the drivers’ real emotions that cannot be completely identified based on their facial expressions. The proposed algorithm comprises of two models: (i) facial expression recognition model, which refers to the state-of-the-art convolutional neural network structure; and (ii) sensor fusion emotion recognition model, which fuses the recognized state of facial expressions with electrodermal activity, a bio-physiological signal representing electrical characteristics of the skin, in recognizing even the driver’s real emotional state. Hence, we categorized the driver’s emotion and conducted human-in-the-loop experiments to acquire the data. Experimental results show that the proposed fusing approach achieves 114% increase in accuracy compared to using only the facial expressions and 146% increase in accuracy compare to using only the electrodermal activity. In conclusion, our proposed method achieves 86.8% recognition accuracy in recognizing the driver’s induced emotion while driving situation.

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