CSEIT161219 | Received: 20 October 2016 | Accepted: 30 October 2016 | September-October-2016 [(2)5: 144-148] International Journal of Scientific Research in Computer Science, Engineering and Information Technology © 2016 IJSRCSEIT | Volume 1 | Issue 2 | ISSN : 2456-3307 144 Multiple Eye Detection and Tracking Using Competitive Approaches In Driver Analysis Tina Trueman 1 , Gowthami J 2 1 Teaching Fellow, Department of Information Science and Technology 2 PG Student, Department of Computer Science and Engineering Anna University, Chennai, tamil Nadu, India ABSTRACT In this paper the Reliable detection and tracking of eyes is an important requirement for attentive user interfaces. It mainly applied in driver analysis. The system uses a small monochrome security camera that points directly towards the driver’s face and monitors the driver’s eyes in order to detect fatigue. In such a case when fatigue is detected, a warning signal is issued to alert the driver. This work describes how to find the eyes, and also how to determine if the eyes are open or closed. If the eyes are found closed for three consecutive frames, the system draws the conclusion that the driver is falling asleep and issues a warning signal. And we present an innovative approach to the problem of eye tracking. They are Region Based Segmentation and Region growing algorithm. A number of traditional eye detectors, chosen for their own properties, are combined by this two different competitive schemes with the aim to obtain a higher degree of robustness and reliability. Keywords: Boosting, Competitive approach, Tracking I. INTRODUCTION An image is digitized to convert it to a form which can be stored in a computer's memory. Once the image has been digitized, it can be operated upon by various image processing operations. Eye detection and tracking can be classified as  Shape-based,  Feature-based,  Appearance-based. II. METHODS AND MATERIAL System Design This describes about the system architecture and individual module diagrams, which deal with a detection-based statistically motivated redundancy exploiting the wiener filter where the parameters of the method are learned from photo metrically, geometrically, graphically similar patches. 2.1 System Architecture Figure 1. System Architecture This approach is not constrained to the specific application to eye tracking and therefore should be in principle applicable to other tracking cases.