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.

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Eye blink detection has gained a lot of interest in recent years in the field of Human Computer Interaction (HCI). Research is being conducted all over the world for developing new Natural User Interfaces (NUI) that uses eye blinks as an input. This paper presents a comparison of five non-intrusive methods for eye blink detection for low resolution eye images using different features like mean intensity, Fisher faces and Histogram of Oriented Gradients (HOG) and classifiers like Support Vector Machines (SVM) and Artificial neural network (ANN). A comparative study is performed by varying the number of training images and in uncontrolled lighting conditions with low resolution eye images. The results show that HOG features combined with SVM classifier outperforms all other methods with an accuracy of 85.62% when tested on images taken from a totally unknown dataset.

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