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

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.

Optical Flow and Driver’s Kinematics Analysis for State of Alert Sensing

“…This is consistent with the knowledge that a drowsy driver will attempt to make fast sudden corrections to deviations from the lane, and it is expectable that the driver will also try to regain the sitting pose quickly, while avoiding to fall asleep. It has been argued, see for example [6,12,13], that monitoring corrections in driving maneuvers and pose changes may not provide information sufficiently in advance to warn the driver. In fact, the evolution of the pose of the driver in time does not seem to provide an indication of fatigue as clearly as the rate of change of the pose, according to our results in Figure 12 for a selection of one awake, one semi-drowsy and one drowsy driver.…”

“…Other methods monitor the driver's steering performance (reaction rates and unexpected lane departures) to warn the driver. However, despite claims that these approaches have low false alarm rates, it is also known that these methods fail to predict micro-sleeps, and there is not enough evidence to support these methods as a reliable way of measuring the driver's state of alert [6,12,13]. Fortunately, there are many behavioral changes that provide reliable visual cues of the driver's state of awareness that can be measured in a non-invasive manner with image processing techniques, namely, eye-blinking frequency and percentage of eyelid closure over time (PERCLOS, [14,15]), yawn frequency, head movement and eye-gaze, among other facial expressions.…”

“…The vision-based approaches must rely on specific techniques to detect the driver's head, face and eyes. Some methods employ intensity and color analysis techniques to segment the parts of the head from the image [13,16–21], while several other approaches rely on the Viola-Jones detector [22–28]. Some approaches only track the eyes, while others focus on particular facial cues, such as yawning [19,29].…”

Memory‐efficient architecture of circle Hough transform and its FPGA implementation for iris localisation

“…Besides iris localisation application, the CHT hardware can also be used in numerous other real‐time image processing and computer vision applications, where problem of detecting round objects is required to be solved such as traffic road sign detection [10], counting and tracking of persons by detecting their heads [11], multi‐robot localisation and tracking [12], pupil localisation in eye‐gaze tracking systems for human–computer interactions [13], eye‐blink and driver‐drowsiness detection [14], ball detection [15] and fruits detection. Most of the above‐mentioned systems are FPGA‐based systems and require compute‐intensive functions to be implemented as dedicated hardware, in order to meet real‐time performance constraint.…”

Eyes Closeness Detection Using Appearance Based Methods

“…The major advantage of this lies in its efficiency and robustness [10] [11]. Current development in computer vision has allowed for robust middle-level feature description for eye patches despite of various changes in appearance, and the remaining variations can be addressed with powerful machine-learning-based classifiers.…”