Intelligent drowsy eye detection using image mining

2015 | Information Systems Frontiers | Citations: 5

Authors: Emam, Ahmed

Abstract: Cell phones, MP4 players, and GPS all distract drivers, forcing them to divide t ... Expand

Abstract: Cell phones, MP4 players, and GPS all distract drivers, forcing them to divide their attention to what is happening outside and what is happening inside the vehicle. Preventing drivers from falling asleep at the wheel and drifting off on the road is an increasingly hard task but a solution can be achieved with today’s technology: by capturing driver’s eye gaze and thus the level of their exhaustion, the potential for safer roads is real. Today, image mining systems can automatically extract important information from an image dataset which can then be used to classify images and image relationship that been applied in different areas. The basic challenge in image mining research area is to determine how image level pixel representation in an original image can be used and constructed to then identify meaningful information or form relationships. In this research Intelligent Drowsy Eye Detection, using an Image Mining (IDEDIM) system is proposed. The proposed architecture would use different feature extraction techniques and three data mining classification techniques to aid with accurate information collection. Two thousand left and right eye images were used to test the developed system by Discrete Wavelet Transform (DWT), Statistical features, and Local binary pattern (LBP) feature extraction techniques. The extracted features were used as input for Decisions Tree C5.0, K Nearest Neighbor (KNN), and the Support Vector Machine (SVM) Classifier. After several experimental sets, the C5.0 and KNN classifier were performing better than SVM classifier. Based on the results, we recommend the inclusion of LBP and DWT in conjunction with C5.0 with KNN as a classification technique. To validate the achieved results, Receiver Operating Characteristic (ROC) curve is used to compare among the proposed classifiers. The proposed system can be integrated with the existing subsystem into real time Drowsy Detection System to achieve excellent accuracy and performance. Collapse

Topics: database system Microsoft SQL server database relational database Visual Basic

Methods: decision tree classification support vector machine machine learning computational algorithm k-nearest neighbor

Theories: statistical learning theory