Anger Detection in Monitoring Drivers’ Facial Expression using Mobile App - Ee, Min Jie (Universiti Malaysia Sarawak (UNIMAS), 2019)
Current facial detection requires experimental set-up which includes usage of variety of camera equipment behind the steering-wheel. This is highly impractical in real-world environment as the set-up might cause annoyance or inconvenience to the driver. Next, steering wheel vibration might induce confusion in drivers. This is because vibrating steering wheel can be caused by faulty brakes, wheel alignment and punctured tires. In order to detect driver’s angry facial expression, image processing algorithm will be applied and implemented in this project. Besides that, an audio feedback feature through mobile application will be implemented as well. With the help of phone camera, driver’s facial expression data can be collected then further analysed via image processing under Microsoft Azure platform. In the end of this project, a working Mobile App should be able to be implemented that can detect angry drivers through monitoring their facial expression. Whenever an angry face is detected, pop-up alert messages and audio feedback will keep reminding drivers to drive calm and safe until drivers manage to handle their emotions where anger is no longer detected.