An Improved Fatigue Detection System Based on Behavioral Characteristics of Driver

TL;DR

This paper presents a driver fatigue detection system using facial feature analysis via a camera, PCA for feature reduction, and SVC for classification, aiming to prevent road accidents caused by drowsiness.

Contribution

It introduces a real-time fatigue detection system combining facial feature analysis, PCA, and SVC, enhancing driver safety by early drowsiness detection.

Findings

Effective detection of driver fatigue through facial features

Reduced feature set with PCA maintains accuracy

System successfully alerts drowsy drivers in tests

Abstract

In recent years, road accidents have increased significantly. One of the major reasons for these accidents, as reported is driver fatigue. Due to continuous and longtime driving, the driver gets exhausted and drowsy which may lead to an accident. Therefore, there is a need for a system to measure the fatigue level of driver and alert him when he/she feels drowsy to avoid accidents. Thus, we propose a system which comprises of a camera installed on the car dashboard. The camera detect the driver's face and observe the alteration in its facial features and uses these features to observe the fatigue level. Facial features include eyes and mouth. Principle Component Analysis is thus implemented to reduce the features while minimizing the amount of information lost. The parameters thus obtained are processed through Support Vector Classifier for classifying the fatigue level. After that classifier output is sent to the alert unit.