Research Aspects - Driver Drowsiness Detection: Systems and Solutions - page 26

as two rectangles vertically adjacent to each other, while darker one is on top of
the brighter one. And in the other case we would need three rectangles adjacent
horizontally to each other, two dark ones for the eyes and one bright one for the
nose in the middle. So the face can be represented with a different combination of
dark and light rectangles. Algorithms using Haar-like features scan throughout the
image, looking for best matches of rectangular patterns.
The most famous face detection algorithm in this category is the Viola-Jones
algorithm [ 24 ]. It is based on exploiting perspective that a face (or any object,
for that matter) can be represented as a collection of dark and bright regions as
explained above. Haar-like features represent relationships between two different
adjacent regions. Since there are many different regions on a human face and every
region has a different relationship with different neighbor regions, the number of
possible combinations is potentially huge. For a detection system to be efficient
and fast it needs to rely on a subset of combinations, focusing on relationships
between adjacent rectangle areas, which will be big enough to efficiently describe
the object of interest. To determine what Haar-like features are significant and
necessary in describing the object, a machine learning algorithm called Adaptive
Boosting (or simply, AdaBoost) is used. The AdaBoost algorithm eliminates all
small, irrelevant features and leaves just the set that is necessary. It has been shown
that only two Haar-like features are enough to describe the human face. The first
feature measures the difference in intensity between the region of the eyes and a
region across the upper cheeks, which shows that eye region is often darker then the
of the upper cheeks. The second feature compares the intensities in the eye regions
to the intensity across the bridge of the nose. Again, eye regions are often darker
then nose region. It was shown that these two are the most consistent features across
big number of images. So if and area of interest shows to contain areas with such
a relationship there is a good possibility that the area of interest contains a human
face (Fig. 4.1 ).
In the driver drowsiness detection literature, there are many solutions which have
adopted this feature extraction algorithm in their systems [ 4 , 8 , 14 , 18 , 26 ].
Skin Color Another popular method used for face detection purposes is the
detection of regions of the image whose color properties are within the range of
color tones associated with the human skin color [ 11 ]. Some researchers in the
field of driver drowsiness detection systems have exploited the knowledge that skin
colors' Red and Green components follow planar Gauss distribution which can be
used as a search parameter for finding a face [ 9 ] while others are using the YCbCr
color model for easier focus on the color of the face while eliminating the intensity
component [ 23 ].
Texture Texture gives us information about the spatial arrangement of color or
intensities in an image or selected region of an image. A human face can be seen as
a composition of micro texture patterns. Eye regions usually contain more fine-
grained texture than, for example, the cheeks. Every part of a human face can
be described with its unique texture qualities. The most commonly used way of
quantifying texture in this domain is by expressing it in terms of Local Binary