Research Aspects - Driver Drowsiness Detection: Systems and Solutions - page 25

4.1.2 Near Infrared (NIR) Cameras

One of the most common limitations of computer vision systems, in general, is their
inability to perform consistently well across a wide range of operating conditions,
e.g., when the lighting conditions are significantly different than the ones for which
the system was designed and tested. In the case of vehicle-mounted solutions that
rely on visual input, the ability to tolerate large variations in light intensity (from
bright sunlight to nighttime driving on unlit roads) presents a formidable challenge.
The solution for ensuring operability in low lighting conditions usually includes
using a NIR camera as a sensor.
The term “near infrared” refers to a small portion of the much larger region
called infrared (IR), located between the visible and microwave portions of the
electromagnetic spectrum. NIR makes up the part of IR closest in wavelength
to visible light and occupies the wavelengths between about 700 and 1,500 nm
(0.7-1.5

m). NIR is not to be confused with thermal infrared, which is on the
opposite end of the infrared spectrum (wavelengths in the (8-15 m) range) and

measures radiant (emitted) heat. NIR cameras are available in either CCD or CMOS
sensors, and they can provide monochrome or color images at their output.
DDD systems that use NIR cameras usually employ an additional source of
NIR radiation, such as LEDs designed to emit radiation in NIR frequency band,
to illuminate the object of interest and, in that way, amplify the input values for the
camera. It is common to have the LEDs focus on the driver's eyes, because of the
pupils' noticeable ability to reflect infrared, which leads to a quick and effective way
to locate the position of the eyes [ 3 , 6 , 7 , 14 ].

4.2 Feature Detection and Extraction

Generally speaking, any system designed to monitor the state of an object of interest
over time must be capable of detecting that object, determining what state the
object is in, and tracking its movements. In computer vision systems, the detection
and tracking steps are based on extracting useful features from the pixel data and
building models that represent the object of interest. In the specific case of DDD
systems whose goal is to determine the drowsiness state of a driver by observing
the driver's facial features, the focus is on the head, face, and eyes. Some of the
most widely used facial features and corresponding feature extraction methods are
described next.

Light Intensity Differentiation The grayscale representation of a face can be
described as a collection of dark and bright areas, where usually the region of the
eyes is much darker then the region of the nose and cheeks and the eyes are darker
then the bridge of the nose. If we take each of these regions and describe them
as a simple rectangle with bright or dark values inside, we are describing them in
terms of Haar-like features [ 24 ]. Put simply, eyes and cheeks can be represented