Examples - Driver Drowsiness Detection: Systems and Solutions - page 37

5.1.1 Head Pose Estimation

A driver's head pose can provide significant amounts of information regarding the
driver's internal state, level of attention and potential sleep deprivation.
The work by Zhang et al. proposes that monitoring driver's head pose and
orientation can give enough clues to predict the driver's intent [ 21 ]. In order to
determine the drivers' head pose, the driver's face has to be detected first. This is a
typical primary step in any behavioral methodology based on monitoring a subject's
face. The face detection algorithm proposed by Viola and Jones [ 18 ] has become
a reference upon which other face detection methods can be built. Zhang et al.
trained three classifiers (focused on horizontal rotation of the driver's head) in order
to successfully detect front-facing, left-facing and right-facing faces. The way to
determine the head's pose and orientation is by analyzing isophote features [ 8 ]of
the driver's head. The isophote properties can capture important information about
the face, such as direction, orientation and curvature. Two histograms are obtained
from the isophote features: a histogram of direction and a histogram of curvature.
The histograms' bin counts serve as the input data to a K-Nearest Neighbor (KNN)
classifier [ 1 ] that will determine where the driver's head is facing at, relative to the
camera.
Chutorian and Trivedi [ 13 ] created a three-part system capable of performing in
real-time (i.e, at 30 frames per second) that can detect the driver's head, provide
initial head pose estimation and continuously track the head's position and orienta-
tion. Similarly to the previously explained work, face detection is the first step of
the system's architecture. Three Adaboost cascades are created to encompass left-
facing, front-facing and right-facing faces in relation to the recording camera since
they are the most commonly occurring poses of driver's head. Once successfully
detected, facial region features are expressed as a localized gradient orientation
(LGO) histogram [ 11 ] which has been shown to be invariant to image's scale and
rotation as well as robust in relation to variations in illumination and noise. The
LGO histogram is used as an input to a Support Vector Regression classifier [ 4 ],
that can provide the driver's current head pose. One of the most interesting aspects
of their work is that it is leaning on the use of augmented reality, by using a virtual
environment that simulates the view space of the real camera [ 12 ].

5.1.2 Yawning

The capacity to estimate whether a driver is yawning and inferring, based on the
frequency of yawning, whether he or she may be too drowsy to drive constitutes
a challenging research problem. The ability to detect yawning state from input
captured by a camera requires detecting features and learning states based on the
relative position and state of the mouth and eyes. In recent years, several researchers
have been working on the topic; two of those efforts are briefly summarized below.