Design and implementation of a real time and train less eye state recognition system

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Eye state recognition is one of the main stages of many image processing systems such as driver drowsiness detection system and closed-eye photo correction. Driver drowsiness is one of the main causes in the road accidents around the world. In these circumstances, a fast and accurate driver drowsiness detection system can prevent these accidents. In this article, we proposed a fast algorithm for determining the state of an eye, based on the difference between iris/pupil color and white area of the eye. In the proposed method, vertical projection is used to determine the eye state. This method is suitable for hardware implementation to be used in a fast and online drowsiness detection system. The proposed method, along with other needed preprocessing stages, is implemented on Field Programmable Gate Array chips. The results show that the proposed low-complex algorithm has sufficient speed and accuracy, to be used in real-world conditions.

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Publié par
Publié le 01 janvier 2012
Nombre de visites sur la page 19
Langue English
Signaler un problème
Dehnavi and Eshghi EURASIP Journal on Advances in Signal Processing 2012, 2012 :30 http://asp.eurasipjournals.com/content/2012/1/30
R E S E A R C H Open Access Design and implementation of a real time and train less eye state recognition system Mohammad Dehnavi * and Mohammad Eshghi
Abstract Eye state recognition is one of the main stages of many image processing systems such as driver drowsiness detection system and closed-eye photo correction. Driver drowsiness is one of the main causes in the road accidents around the world. In these circumstances, a fast and accurate driver drowsiness detection system can prevent these accidents. In this article, we proposed a fast algorithm for determining the state of an eye, based on the difference between iris/pupil color and white area of the eye. In the proposed method, vertical projection is used to determine the eye state. This method is suitable for hardware implementation to be used in a fast and online drowsiness detection system. The proposed method, along with other needed preprocessing stages, is implemented on Field Programmable Gate Array chips. The results show that the proposed low-complex algorithm has sufficient speed and accuracy, to be used in real-world conditions. Keywords: eye state, pupil, iris, drowsiness, vertical projection, FPGA
Introduction In order to determine state of an eye, authors of [5] All over the world and every day, driver s fatigue and propose a method based on combination of projection drowsiness have caused many car accidents. In fact, and the geometry feature of iris and pupil. Authors of drowsiness is the case of about 20% of all car accidents [6,7] use the fact that the iris and pupil are darker than in the world [1,2]. As a result, an electronic device to skin and white part of the eye. Authors of [11] proposed control the driver s awareness is needed. This device an algorithm based on the cascade AdaBoost classifier. should monitor and detect the driver s drowsiness In [12], a gray level image of an eye is converted to a online and activate an alarm system immediately. binary image, using a predetermined threshold. Then, In recent years, many researches on these systems based on the number of black and white pixels of this have been done and their results are reported [3-12]. binary image, state of the eye is determined. One of these methods is to monitor the movement of The algorithm presented in [8] used the Hough the vehicle to detect drowsiness of the driver [3]. This Transform to detect the iris and to determine openness method depends very much to the type of vehicle and of the eye. Authors of [13] used three steps to recognize the condition of road. Another method is to process the the eyes state. In the first step, the circular Hough electrocardiogram (ECG) signals of driver [4]. In this transform is used to detect the circle of an iris in the system, some ECG probes are needed to be connected image of an open eye. If this circle is not found then in to the driver, which are disturbing the driver. There are the second step, the direction of the image of upper eye-other methods based on processing of the image of dri- lid is obtained to determine whether it is below of the ver s face and eye. Some of methods in this category are line between two corners of an eye, to detect a closed to process the image of driver and to monitor his/her eye. If a closed or open eye is not determined in the eye blinking [5-11]. In these systems, the face process, first two steps, then in the third step, the standard eye region detection process, and eye state recognition deviation of distance between upper and lower eyelids is process are performed. obtained and is compared to a threshold to determine the eye state. Some researches are based on the projection of the * Correspondence: mo.dehnavi@mail.sbu.ac.ir image, to determine the state of an eye. In [9], the ECE Department, Shahid Beheshti University, Tehran, Iran © 2012 Dehnavi and Eshghi; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.