Detection of drowsiness based on HOG features and SVM classifiers

This paper presents an accurate method of drowsiness detection for the images obtained using low resolution consumer grade web cameras under normal lighting conditions. The drowsiness detection method uses Haar based cascade classifier for eye tracking and combination of Histogram of oriented gradient (HOG) features combined with Support Vector Machine (SVM) classifier for blink detection. Once the eye blinks are detected then the PERCLOS is calculated from it. If the PERCLOS value is greater than 6 seconds then the person is said to be drowsy. The presented system was validated by comparing the prediction of the system with that of a human rater. The system matched with the human observer with 91.6 % accuracy.