Driver Drowsiness Detection and Measurement Methods - Driver Drowsiness Detection: Systems and Solutions - page 12

2.4 Behavioral Methods

The methods mentioned thus far were deemed as either unreliable or very intrusive
for real-world applications, thus leading towards exploiting a different type of
methodology, based upon non-invasive observation of a driver's external state.
These methods are based on detecting specific behavioral clues exhibited by a driver
while in a drowsy state. A typical focus is on facial expressions that might express
characteristics such as: rapid, constant blinking, nodding or swinging of the head, or
frequent yawning. These are all tell-tale signs that a person might be sleep deprived
and/or feeling drowsy. Typically, systems based on this methodology use a video
camera for image acquisition and rely on a combination of computer vision and
machine learning techniques to detect events of interest, measure them, and make a
decision on whether the driver may be drowsy or not. If the sequence of captured
images and measured parameters (e.g., pattern of nodding or time lapsed in “closed
eye state”) suggest that the driver is drowsy, an action—such as sounding an audible
alarm—might be warranted.

￿ Head or eye position . When a driver is drowsy, some of the muscles in the body

begin to relax, leading to nodding. This nodding behavior is what researchers
are trying to detect. Research exploiting this feature has started just recently
[ 47 , 69 ]. Detecting head or eye position is a complex computer vision prob-
lem which might require stereoscopic vision or 3D vision cameras.

￿ Ya w n i n g . Frequent yawning is a behavioral feature that tells that the body

is fatigued or falling into a more relaxed state, leading towards sleepiness.
Detecting yawning can serve as a preemptive measure to alert the driver. It should
be noted, however, that yawning does not always occur before the driver goes into
a drowsy state. Therefore it cannot be used as a stand-alone feature; it needs to
be backed up with additional indicators of sleepiness. Examples of research in
this area include the work of Smith, Shah, and da Vitoria Lobo [ 56 ], and more
recently by Saradadevi et al. [ 55 ].

￿ Eye state . Detecting the state of the eyes has been the main focus of research for

determining if a driver is drowsy or not. In particular, the frequency of blinking
has been observed [ 1 , 5 , 11 , 13 , 39 , 44 ]. The term PERCLOS (PERcentage of eye-
lid CLOSure over the pupil over time) has been devised to provide a meaningful
way to correlate drowsiness with frequency of blinking. This measurement has
been found to be a reliable measure to predict drowsiness.
At any given time, the eye can roughly be categorized into one of three states:
wide open, partially open, or closed. The last two can be used as indicators
that a driver is experiencing sleepiness. If the eyes stay in these two states for
a prolonged period of time, it can be concluded that the driver is experiencing
abnormal behavior. An eye-state detection system must be able to reliably
detect and distinguish these different states of the eyes. Various algorithms with
various approaches for extracting and filtering important features of the eyes
[ 33 , 35 , 52 , 53 , 64 ] have been used throughout the years. Typically, the feature