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Title: Emotion recognition system with facial expression system for a vehicle to make the journey harmless
Authors: Mohamed Naleer, Haju Mohamed
Narmadi Wathsala, Dissanayake
Keywords: Highways Traffic Surveillance System
IP camera
Issue Date: 25-Jun-2020
Publisher: Faculty of Management and Commerce South Eastern University of Sri Lanka
Citation: Journal of Information Systems & Information Technology Vol. 5 No. 1, 2020 pp. 45-55.
Abstract: Image processing is commonly used to recognize objects. It can be used to detect human facial expressions and emotions by observing facial features. Capturing emotions depends on many tools and conditions such as brightness, camera, changing rate of facial features etc. Emotion recognition is software that allows program to “read” the emotions on the human face using advanced image processing. In order to understand not only what a person’s face or image looks like, but also how it looks. Emotion recognition has been applied in many fields such as Medical, Security, and Business etc. There are many difficulties in developing a good emotion recognition system for the human face in real time. Since most of the time facial features of expression and the style of showing emotion to the outside world is different from person to person. Therefore, it is very difficult to build an accurate system for real time emotion recognition. This project is to detect human facial expressions to predict the current emotional state. The system specially focused on reducing fatal road accidents due to drivers' state of emotion. Initially it is built to recognize the human emotion through facial expressions and then evaluated to detect drowsiness using facial landmarks to ensure the safety of the driver. Training has been done with Kaggle dataset for seven emotional states (Neutral, Happy, Angry, Sad, Scared, Surprised and Disgust) called universal emotions. In order to predict drowsiness, it uses specific 12 points on face (6 points on each eye) in shape predictor 68 face landmarks. Evaluated system has given 66% accuracy in testing and drowsiness alert also showed a very good success rate.
ISSN: 24780677
Appears in Collections:Vol.5 No.1 (2020)

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