Study On The Face Recognition Algorithm Based On Infrared Technology For Driver

Nowadays statistics in many countries show that death traffic accident at night is directly or indirectly due to drivers focused their attention, caused by fatigue or sleepiness. Driver's factors have been one of the most important causes of road accidents. Until now many research have focused on monitoring the driver's face, eye, pupil and so on to obtain his/her face rotation and orientation, eye activities, eye blinking rate, gaze direction, finally to determine his/her fatigue or distraction state. However, Most of researchers have neglected driver's fatigue state such as driver's yawning and his/her distraction like conservation and talking on a cellular phone while his/her driving in the evening. Driver monitoring has been a focus of Safety Driving Assist technologies research. In Which, fatigue driving and driving spirit scattered condition monitoring system will play an important role in lowering the accident rate at night. Machine Vision based on infrared technology in real-time, accuracy, and applicability of economic and other aspects has greater advantages than other monitoring methods at night. Study on driver visual monitoring Using vehicle-mounted camera systems is the hot technologies today. Many researchers focused on tracking the driver through the face, eyes, the pupil, has been head rotation and direction eyelid movement blink frequency, driver fatigue monitoring the direction of attention or mental scattered. However, the driver Yawns while driving or driving fatigue did not receive the spirit of scattered attention in the evening, We can also detected by the driver, the driver's eyes to fatigue and mental state decentralized monitoring. Driver's eyes detection and location technology has a direct impact on the state of the driver eye detection. According to the analysis of the state of the driver's eyes, this paper proposes Several methods of driver's eye detection and location, which lays a foundation for driver monitoring based on infrared technology for further study and provides reference information and support for driver monitoring technology of the integrated monitoring system. The research of the paper includes several parts : Pre-processing of driver's face infrared images, face detection segmentation ,eyes detection and location .1. Pre-processing algorithms of driver's infrared images are studied. At first the images are changed into gray from true color. Second, median filtering method and average filtering method are introduced. Median filtering method is better than average filtering method by comparison. Experimental results also show that the algorithm is effective and reliable.2. Driver's gray images normalized by similarity are segmentalized by the maximum variance of the similarity threshold segmentation method and the moment invariants algorithm. By comparison of experiment , the maximum variance of the similarity threshold segmentation method has more perfect effect than the moment invariants algorithm. Then Using Connected component labeling algorithm to locate driver's faces. Using Projection and the relationship facial geometry to determine the regions of face in order to determine face's location. Experimental results show that: This type of driver's face detection and location has high reliability, real-time. It has good dynamic positioning capability, and has better adaptability for different infrared image, complex background, and the driver sitting positions. It lays a good foundation for eyes detection and monitoring.3. When the images are segmentalized , eyes cavity and the surrounding region of the face skin has a high contrast. According to these features we analyze the characteristics of each type of image segmentation algorithm effectively. For the feature of the face image the ideal method of segmentation is the segmentation algorithm based on the character of eyes ellipse macula. So the character variance is used to confirm eyes information .In fact this method can do favor to eyes location . 4. Driver's eyes location adopts Morphological processing algorithms to remove the noise in the light of the eyes images after segmentation. Then connected component labeling algorithm is utilized to mark areas of interest images of driver's eyes. The results show that: the above methods could better locate the eyes under different circumstances.All necessary software is developed using Visual C++ and Windows 2000. The software realizes various algorithm functions. The experimental results show that the algorithms have good performance and good robustness.