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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.
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Emam, A. (2012). Robust real time drowsy eye detection system. International Journal of Computers and Their Applications.
Hsu, W., Lee, M. L., & Zhan, J. (2002). Image mining: trends and developments. Journal of Intelligent Information Systems, 19(1), 7–23. CrossRef
Umamaheswari, K., Sumathi, S., Sivanandam, S. N., & Ponson, T. (2007). Artificial neural network based image data mining with hybrid recognizers. International Journal of Soft Computing Applications, EuroJournals Publishing, Inc, 27–49.
Conci, A., Mathias, E., & Castro, M. M. Image mining by color content. Expert Systems with Applications, 23(4), 377–383.
Zhang, J., Hsu, W., & Lee, M. L. (2001). Image mining: Issues, frameworks and techniques, The 2nd, Int. Workshop Multimeida Data Mining (MDM/KDD), San Francisco, USA.
Zhang, J., Hsu, W., & Lee, M. L. (2001). An information-driven framework for image mining (pp. 232–242). 12th International Workshop on Database and Expert Systems Applications (DEXA 2001), 3–7 September, Munich, Germany.
Pradeep Kumar, P. Nagabhushan, “Multiresolution Knowledge Mining using Wavelet Transform”, Engineering Letters, 14:1, EL_14_1_30 (Advance online publication: 12 February 2007)
Lakshmi Devasena, C., Sumathi, T., & Hemalatha, M. (2011). An experiential survey on image mining tools, techniques and applications. International Journal on Computer Science and Engineering (IJCSE), 3(3)
Shylaja, S., Balasubramanya Murthy, K. N., Natarajan Nischith, S., Muthuraj, R., & Ajay, S. (2011). Feed Forward Neural Network Based Eye Localization and Recognition Using Hough Transform. International Journal of Advanced Computer Science and Applications (IJACSA), 2(3), 104–109.
Janani, M., & Manicka Chezian, R. (2012). A SURVEY ON CONTENT BASED IMAGE RETRIEVAL SYSTEM. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) ISSN: 2278 – 1323, 1(5), 266–269.
Sudhir, R. (2011). A Survey on Image Mining Techniques: Theory and Applications. Computer Engineering and Intelligent Systems, 2(6), 44–53. ( www.iiste.org) ISSN 2222-1719.
Wang, J.-M., Lin, H.-W., Fang, C.-Y., & Chen, S.-W. Detecting driver’s eyes during driving (pp. 941–947). 18th IPPR Conference on Computer Vision, Graphics and Image Processing (CVGIP 2005) 2005/8/21~23, Taipei, ROC.
Moudani, W., & Sayed, A.-R. (2011). Efficient Image Classification using Data Mining. International Journal of Combinatorial Optimization Problems and Informatics, 2(1), 27–44. ISSN: 2007-1558.
Deshmukh, R. K., & Bahendwar, Y. S. (2012). MR image segmentation using wavelet and watershed transforms. International Journal of Societal Applications of Computer Science, 1(2), 66–70. ISSN 2319 – 8443.
Minkov, K., Zafeiriou, S., & Pantic, M. (2012). A comparison of different features for automatic eye blinking detection with an application to analysis of deceptive behavior, Proceedings of the 5th International Symposium on Communications, Control and Signal Processing, ISCCSP, Rome, Italy, 2–4 May 2012.
Zhou, L., & Wang, H. (2011). Open/closed eye recognition by local binary increasing intensity patterns (pp. 6–11). IEEE 5th International Conference on Robotics, Automation and Mechatronics (RAM).
Dong, W., & Qu, P. (2009). Eye state classification based on multi-feature fusion (pp. 231–234). Chinese Control and Decision Conference (CCDC).
Blowmick, B., & Chidaraand Kumar, K. (2009). Detection and classifications of eye state in IR camera for the driver drowsiness identification. IEEE International Conference on Signal and Image Processing Applications, 340–345.
Wang, H., Zhou, L. B., & Ying, Y. (2010). A novel approach for real time eye state detection in fatigue awareness system. IEEE Conference on Robotics, Automation and Mechatronics, 528–532.
Coetzer, R. C., & Hancke, G. P. (2011). Eye detection for a real-time vehicle driver fatigue monitoring system (pp. 66–71). IEEE Intelligent Vehicles Symposium (IV) Baden-Baden, Germany, June 5–9, 2011.
Ahonen, T., Hadid, A., & Pietikainen, M. (2004). Face recognition with local binary patterns. In Proc. ECCV.
S. Kotz, N. L. Johnson, “Encyclopedia of Statistical Sciences” , John Wiley & Sons, New York, NY, 1982-1989.
Xu, X., & Zhang, Q. (2009). Medical image retrieval using local binary patterns with image euclidean distance (pp. 1–4). In International Conference on Information Engineering and Computer Science, 2009. ICIECS 2009.
Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1), 51–59. CrossRef
Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. New York: Springer-Verlag. CrossRef
Han, J., Kamber, M., & Pie, J. (2005). Data mining: concepts and techniques, second edition- 2 edition -The Morgan Kaufmann Series in Data Management Systems-, ISBN-10: 1558609016.
Castleman, K. R. (1996). Digital image processing. Prentice Hall.
Wang, S. Y. X., Han, T. X. (2009). An HOG-LBP human detector with partial occlusion handling. In ICCV.
Video for Windows (2010). MSDN or Micro Soft Developer Network. Microsoft Corporation, http://msdn.microsoft.com/en.
Foschi, P. G., Kolippakkam, D., Liu, H., Mandvikar, A. (2002). Feature extraction for image mining. International Workshop on Multimedia Information Systems (MIS), October 10 - November 1, Tempe, Arizona, USA.
Kumar, V., Kumar, A., & Bhardwaj, A. (2011). Image Search using Overlapping of Different Image Features. International Journal of Computer Applications (0975 – 8887), 30(11), 32–36. CrossRef
Liu, H., Motoda, H., Setiono, R., & Zhao, Z. (2010). Feature selection: an ever evolving frontier in data mining. Journal of Machine Learning Research (JMLR) - The Fourth Workshop on Feature Selection in Data Mining Proceedings Track.
Emam, A., & Reyad, Y. A. (2012). EYE IMAGE MINING. International Journal of Societal Applications of Computer Science, 1(1), 35–38. www.ijsacs.org.
www.jrocfit.org by John and Russell from department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
Duda, R. O., & Hart, P. E. (1973). Pattern Classification and scene analysis. New York: Wiley.
M. Pazzani, C. Merz, P. Murphy, K. Ali, T. Hume, and C. Brunk, ‘Reducing misclassification costs’, in Proceedings of the Eleventh International Conference on Machine Learning, pp. 217–225, San Francisco, (1994). Morgan Kaufmann.
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