IEEE Consumer Electronics Magazine - October 2015 - 114
Fusion of camera information
and sensors to measure the driver's
steering behavior are combined to
determine the driver's drowsiness
in real time.
tracking, eye-area detection and tracking, upper and lower
eyelid detection, and eye feature measurements. Figure 3 represents the flow diagram to be followed to detect most eye
features (i.e., blinking frequency or PERCLOS). This way, it
would be easier to determine the elements to take care of and
define them [23], [30], [34]. As shown in Figure 3, the strategy can be divided into five general stages.
CAPTURE FACIAL IMAGES
Facial image capture could be integrated into other stages,
such as image acquisition, enhancement, and restoration
[70]. Normally, most of the existing systems use different
types of charge-coupled devices (CCDs), including infrared,
to capture face images [14], [34], [71], [72]. In addition, the
usage of the complementary metal-oxide semiconductor
(CMOS) is becoming more popular [20], [33], [40], as the
prices are dropping. The CMOS provides better sensitivity
in the near-infrared light spectrum, but it also has better
timing control [20] compared to the CCD image sensor.
Regarding image acquisition software, concepts such as
Capture Facial Image
Image
Acquisition
Image
Image
Enhancement Restoration
sampling of an image, quantization, and frames per second
are fundamental to correct hardware behavior and to obtain
the expected set of inputs or images [70], [73]. The image
enhancement and restoration stages depend on the quality
of the acquired image.
FACE DETECTION AND TRACKING
There are many options for the type of algorithm to use in the
facial recognition stage. Background characterization is a
very useful method for vehicular system applications,
depending on the camera, because the background is usually
very similar. This way, the face can be segmented and delimited. In the case of Hough transforms, the purpose is to
accomplish the acquisition of main features on the image by
identifying lines. This method is very popular for facial
detection [14], [20], [74]. The wavelet transform is a very
common technique for face detection, especially the Gabor
and Haar wavelets [71]. Even though this option is very common in applications such as compression, it is also very
effective for component or main features analysis. Entropy in
particle filters is based on a Bayesian model and its estimation. The application found in [75] is made for both features:
face and eye tracking. The Haar classifier is mainly a cascade
of boosted classifiers using Haar-like features. This method is
very popular for object detection, including face and eyes.
Some references use this method to detect one or several
faces in one input image [31], [76]-[79]. Skin-color detection/segmentation is used by some articles related to DFD
[30], [35], [82] and by some other applications [76], [83].
This is one of the most popular strategies, since the color of
human skin is distinctive [33]. Most of the strategies evaluate
the skin-color statistics. Skin-modeling is considered to be a
fast color-image-based detection method [76]. Other works
use the Kalman filter. The main purpose of the Kalman filter
is to predict the location of the face's area. Some assumptions
that have to be made are that the system is linear, and the
noise affecting the measurements is white and Gaussian [82].
There are several articles mentioning the usage of such a filter as part of the tracking methodology [31], [83]-[85].
EYE-AREA DETECTION AND TRACKING
Face
Detection and
Tracking
Eye-Area
Detection and
Tracking
Eye Feature(s)
Opening and Closing
of Eyelids
The detection of where the eyes are located includes a considerable quantity of methods. Wavelets could be an option, as this
algorithm verifies the main components of an image [29]. Ellipse
and circle detection are used in [29], [73], and [86]. The Kalman
filter is one of the few filters that obtain data in a future time (nonlinear systems), and it has been used as part of other eye-tracking
methods [36]. Line-detection methods are reported in [73] and
[86]. There are several articles mentioning the usage of such filters
as part of the tracking methodology [31], [83]-[85]. Generally,
these methods are based on geometric methods as they establish
very particular traces from the iris (i.e., quantization vectors).
MEASUREMENT OF OPENING AND CLOSING OF EYELIDS
FIGURE 3. A block diagram of general drowsiness detection
focused on eye features (machine vision main stages).
114 IEEE Consumer Electronics Magazine
^
october 2015
When measuring the opening and closing of the eyelids, the
DFD system must be capable of using one or more methods.
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