Multi-Index Driver Drowsiness Detection Method Based on Driver s Facial Recognition Using Haar Features and Histograms of Oriented Gradients

Abstract

[EN] It is estimated that from 10% to 20% of road accidents are related to fatigue, with accidents caused by drowsiness up to twice as deadly as those caused by other factors. In order to reduce these numbers, strategies such as advertising campaigns, the implementation of tachographs in vehicles used for road transport of goods and passengers, or the use of drowsiness detection systems in cars have been implemented. Within the scope of the latter area, the technologies used are diverse. They can be based on the measurement of signals such as steering wheel movement or vehicle position on the road or on driver monitoring. Driver monitoring is a technology that has been little ex-ploited so far and can be implemented in many different approaches. This work addresses the evaluation of a multidimensional drowsiness index based in the recording of facial expressions, gaze direction and head position, and studies the feasibility of its implementation in a low-cost electronic package. Specifically, the aim is to determine the state of the driver by monitoring their facial expressions, such as the frequency of blinking, yawning, and eye opening, gaze direction and head position. For this purpose, an algorithm capable of detecting drowsiness has been developed. Two different approaches are compared: Facial recognition based on Haar features and facial recognition based on Histograms of Oriented Gradients (HOG). The implementation has been carried out on a Raspberry Pi, a low-cost device that allows to create a prototype that can detect drowsiness and interact with peripherals such as cameras or speakers. The obtained results show that the proposed multi-index methodology performs better in detecting drowsiness compared to algorithms based on one index detection.