11-10-2016, 03:46 PM
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Abstract—
The objective was to develop a system for detecting the closing of eyes of a person driving an automobile and provide an alarm indication thus preventing road accidents from occurring. Live video relay of the driver's eyes is processed using Image Processing in MATLAB to detect whether the eye is closed for more than a fixed duration thus indicating conditions of fatigue, alcohol consumption etc. The system is consisting of web camera which placed in a way that it records driver’s head movements in order to detect drowsiness. As drowsiness is detected, a signal is issued to alert the driver. The system deals with detecting face, eyes and mouth within the specific segment of the image. All the possible actions have been considered and output is generated accordingly. The system proves to be more accurate and safe compared to the existing sleep detection system developed using Infrared Sensors and Micro-processors
Introduction (Heading 1)
In this work we focus on iris liveness detection, i.e., identi- fication of liveness symptoms that could prove the authenticity of the eye and the willingness of the subject to be registered by the sensor. Instead of more commonly used static properties of the eye or its tissue, we use dynamics of the pupil registered under visible light stimuli. Since the pupil reacts involuntarily when the light intensity changes, it is difficult to conceal this phenomenon. As will be shown in the paper, the pupil dynamics are not trivial, making it difficult to mimic them for artificial objects. In our tests we decided not to use static objects such as iris paper printouts or patterned contact lenses, since in such cases we would be assured of success (static objects do not present significant dynamics, apart from some measurement noise, and thus are easily recognizable when dynamics is the key). Instead, to assess the proposed method performance, we classify spontaneous pupil oscillations (often called hippus) and normal pupil reactions to a positive surge of visible light, thus making the tests more realistic. To our best knowledge, this is the only work that employs pupil dynamics for liveness detection and which is evaluated on dynamic, real objects rather than static artifactsicro-processors.
RELATED WORKS:
LITERATURE SURVEY:1
In this contribution we present a visual driver surveillance system to monitor the driver’s head morion as well as the eye blink patterns. Based on these measun?d features the system is able to detect symptoms of farigire and monotony. The main advantages of the presented system in coiitrast to existing ones is the usage of standard equipment to achieve a good cost-performance ratio, fast compiitarion time, the possibility of measurements in darkness and the consideration of monotony. rite image analysis is realized in a coarse-to-fine architecture. Atfrsr the driver’s face is detected which is based on a boosted cascade of Haar wavelets. Then the eyes are searched in the face and occurring eye blinks measured by analyzing the opticalflow of the qes’ region. The perforrnance of the sysreni was rested successfully under ideal and natural conditions.
LITERATURE SURVEY 2:
Human errors are the cause of most traffic accidents, with drivers’ inattention and wrong driving decisions being the two main sources. These errors can be reduced, but not completely eliminated. That is why Advanced Driver Assistance Systems (ADAS) can reduce the number, danger and severity of traffic accidents. Several ADAS, which nowadays are being researched for Intelligent vehicles, are based on Artificial Intelligence and Robotics technologies. In this article a research platform for the implementation of systems based on computer vision is presented, and different visual perception modules useful for some ADAS such as Line Keeping System, Adaptive Cruise Control, Pedestrian Protector, or Speed Supervisor, are described.
LITERATURE SURVEY 3:
As computer vision based systems like lane tracking, face tracking and obstacle detection mature an enhanced range of driver assistance systems are becoming feasible. This paper introduces a list of core competencies required for a driver assistance system, the issue of building in robustness is highlighted in contrast to leaving such considerations to a later product development phase. We then demonstrate how these issues may be addressed in driver assistance systems based primarily on computer vision. The underlying computer vision systems are discussed followed by an example of a driver support application for lane keeping based on force-feedback through the steering wheel.
EXISTING SYSTEM:
To detecting drowsiness it is necessary to know eye state that is open or close. Eye state classification is difficult due to some parameters. According to the efficiency and low computational time of Support Vector Machine (SVM), proposed system use this method to analysis eye state. After eye has been detected, LBP operator has been used to extract eye characteristics.Here LBP is expressed as Local Binary Pattern..
PROPOSED WORK:
PROPOSED SYSTEM:
Locate, track and analyze both the driver's face and eyes to compute a drowsiness index to prevent accidents. Every drowsiness detection system has several main modules such as face and eye detection, tracking, etc. A new algorithm for detecting iris, pupil and lips based on color information is proposed. The percent of detection of iris, pupil will decide the eye state. According to the identification, vehicle is controlled by speed.
Abstract—
The objective was to develop a system for detecting the closing of eyes of a person driving an automobile and provide an alarm indication thus preventing road accidents from occurring. Live video relay of the driver's eyes is processed using Image Processing in MATLAB to detect whether the eye is closed for more than a fixed duration thus indicating conditions of fatigue, alcohol consumption etc. The system is consisting of web camera which placed in a way that it records driver’s head movements in order to detect drowsiness. As drowsiness is detected, a signal is issued to alert the driver. The system deals with detecting face, eyes and mouth within the specific segment of the image. All the possible actions have been considered and output is generated accordingly. The system proves to be more accurate and safe compared to the existing sleep detection system developed using Infrared Sensors and Micro-processors
Introduction (Heading 1)
In this work we focus on iris liveness detection, i.e., identi- fication of liveness symptoms that could prove the authenticity of the eye and the willingness of the subject to be registered by the sensor. Instead of more commonly used static properties of the eye or its tissue, we use dynamics of the pupil registered under visible light stimuli. Since the pupil reacts involuntarily when the light intensity changes, it is difficult to conceal this phenomenon. As will be shown in the paper, the pupil dynamics are not trivial, making it difficult to mimic them for artificial objects. In our tests we decided not to use static objects such as iris paper printouts or patterned contact lenses, since in such cases we would be assured of success (static objects do not present significant dynamics, apart from some measurement noise, and thus are easily recognizable when dynamics is the key). Instead, to assess the proposed method performance, we classify spontaneous pupil oscillations (often called hippus) and normal pupil reactions to a positive surge of visible light, thus making the tests more realistic. To our best knowledge, this is the only work that employs pupil dynamics for liveness detection and which is evaluated on dynamic, real objects rather than static artifactsicro-processors.
RELATED WORKS:
LITERATURE SURVEY:1
In this contribution we present a visual driver surveillance system to monitor the driver’s head morion as well as the eye blink patterns. Based on these measun?d features the system is able to detect symptoms of farigire and monotony. The main advantages of the presented system in coiitrast to existing ones is the usage of standard equipment to achieve a good cost-performance ratio, fast compiitarion time, the possibility of measurements in darkness and the consideration of monotony. rite image analysis is realized in a coarse-to-fine architecture. Atfrsr the driver’s face is detected which is based on a boosted cascade of Haar wavelets. Then the eyes are searched in the face and occurring eye blinks measured by analyzing the opticalflow of the qes’ region. The perforrnance of the sysreni was rested successfully under ideal and natural conditions.
LITERATURE SURVEY 2:
Human errors are the cause of most traffic accidents, with drivers’ inattention and wrong driving decisions being the two main sources. These errors can be reduced, but not completely eliminated. That is why Advanced Driver Assistance Systems (ADAS) can reduce the number, danger and severity of traffic accidents. Several ADAS, which nowadays are being researched for Intelligent vehicles, are based on Artificial Intelligence and Robotics technologies. In this article a research platform for the implementation of systems based on computer vision is presented, and different visual perception modules useful for some ADAS such as Line Keeping System, Adaptive Cruise Control, Pedestrian Protector, or Speed Supervisor, are described.
LITERATURE SURVEY 3:
As computer vision based systems like lane tracking, face tracking and obstacle detection mature an enhanced range of driver assistance systems are becoming feasible. This paper introduces a list of core competencies required for a driver assistance system, the issue of building in robustness is highlighted in contrast to leaving such considerations to a later product development phase. We then demonstrate how these issues may be addressed in driver assistance systems based primarily on computer vision. The underlying computer vision systems are discussed followed by an example of a driver support application for lane keeping based on force-feedback through the steering wheel.
EXISTING SYSTEM:
To detecting drowsiness it is necessary to know eye state that is open or close. Eye state classification is difficult due to some parameters. According to the efficiency and low computational time of Support Vector Machine (SVM), proposed system use this method to analysis eye state. After eye has been detected, LBP operator has been used to extract eye characteristics.Here LBP is expressed as Local Binary Pattern..
PROPOSED WORK:
PROPOSED SYSTEM:
Locate, track and analyze both the driver's face and eyes to compute a drowsiness index to prevent accidents. Every drowsiness detection system has several main modules such as face and eye detection, tracking, etc. A new algorithm for detecting iris, pupil and lips based on color information is proposed. The percent of detection of iris, pupil will decide the eye state. According to the identification, vehicle is controlled by speed.