Examples - Driver Drowsiness Detection: Systems and Solutions - page 39

given geometrical and statistical restrictions—the movement of the eyelids can be
detected and used as a measure of eye closeness. The frequency of blinking is used
as a measurement of how fatigued is the driver. The algorithm can track eyes in
higher speeds after initial eye detection and is gender independent as well as resilient
towards low levels of lighting.
TheworkbyWuetal.[ 20 ] also determines driver's drowsiness by monitoring
state of the eyes of a driver. Their detection process is divided into three stages:
(i) initial face detection based on classification of Haar-like features using the
AdaBoost method; (ii) eye detection by SVM classification of eye candidates
acquired by applying radial-symmetry transformation to the detected face from
the previous stage; and (iii) extraction of local binary pattern (LBP) feature out
of left eye candidate, which can be used to train the SVM classifier to distinguish
between open eye state and closed eye state. The LBP of an eye can be thought
of as a simplified texture representation of it. Both closed- and open-eye LBPs are
distinguishable by two-state classifiers such as the SVM.
Tian and Qin [ 17 ] propose that combining several basic image processing
algorithms can increase the performance of eye state detection system and bring
it closer to real time performance. Moreover, two different methodologies are
proposed for daytime and nighttime detection. In order to detect the driver's face
during daytime, a combination of skin color matching with vertical projection
is performed. This color matching will not work during night, therefore vertical
projection is performed on the cropped face region acquired after adjusting image
intensity to have uniform background. For locating the eyes, horizontal projection is
applied on the previously detected face. The eye region is then converted to a binary
image in order to emphasize the edges of the eyes. The converted image is processed
through a complexity function, which provides an estimate of its complexity level:
an image containing open eyes has more complex contours than another image
containing closed eyes. The difference in complexity level can be used to distinguish
between those two states.
Inafollow-upwork[ 5 ], Hong, Qin, and Sun introduced a few modifications to
their original methodology. To increase success rate of face detection, an optimized
Haar-like feature approach is used, due to its better detection rate and ability to
reduce the number of false positives. An AdaBoost classifier is fed with both the
results of applying a Canny edge detector to the image as well as the original
image, resulting in increased face detection performance. An additional change
that consequently provides better results in the eye detection state is that the
detected face image is modified to eliminate the unwanted area about the head
that can skew the results of horizontal projection. Additionally, smoothing part
of the horizontal projection curve is eliminated, thereby increasing the speed of
the algorithm. A complexity function is adopted to be able to compensate for
environmental changes. Combined, all the optimization changes have increased the
speed of the overall system to approach real-time performance.
Wang et al. [ 19 ] addresses the eye state detection problem by extracting
discriminative features of the eye with unique intensity spatial correlation, such
as the color correlogram [ 6 ], and using a reliable machine learning classification