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A Driver Drowsiness Detection
Based on An Active IR illumination
Student: Kuang-Siyong Chang
Advisor: Prof. Din-Chang Tseng
June 2005
Institute of Computer Science and Information Engineering
National Central University
Chung-li, Taiwan 320
Submitted in partial fulfillment of the requirements for the degree of Master of
Computer Science and Information Engineering from National Central University
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- ii -
Abstract
An active computer vision system is proposed to extract various visual
cues for drowsiness detection of drivers. The visual cues include eye close/
open, eye blinking, eyelid movement, and face direction.
The proposed system consists of four parts: an active image acquisition
equipment, eye detector, eye tracker, and visual cue extractor. For working in
various ambient light conditions, we used an IR camera equipped with a
blinkingIRilluminator to acquire derivers pupils and face for detecting and
tracking eyes.
The bright and dark pupil images acquired by the active equipment share
the same background and external illumination; we can simply subtract the
two images to extract pupils. Based on the location of a pupil, the eye region
is clipped to be verified by the SVM method. If the detection is success in
consecutive three frames, the procedure is turned to tracking phase. There are
two stages in the tracking phase. The first-stages method is the same the
detection method. If it is fail, the second tracking strategy is launched based
on the matching principle.
In experiments, we conduct several experiments with various ambient
light conditions, such as day and night to evaluate the proposed system. From
the experimental results, we find that the proposed approach can accurately
detect and track eyes in the various ambient light conditions.
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Contents
Abstract ......................................................................................................... ii
Contents ........................................................................................................ iii
List of Figures ............................................................................................... v
List of Tables ............................................................................................... vii
Chapter 1 Introduction .................................................................................. 1
1.1 Motivation ........................................................................................ 1
1.2 System overview .............................................................................. 2
1.3 Thesis organization ........................................................................... 2
Chapter 2 Related Works ............................................................................... 42.1 Techniques for detecting drowsiness ................................................ 4
2.2 Existed drowsiness detection systems .............................................. 5
2.2.1 Drivers feature detection ....................................................... 5
2.2.2 Drowsiness judgment ........................................................... 11
Chapter 3 An Active Image Acquisition Equipment ................................... 13
3.1 Introduction of bright/dark pupil phenomenon .............................. 13
3.2 Two-ringIRilluminator .................................................................. 15
3.3 Hardware and software architectures ............................................. 18
Chapter 4 Eye Detection ............................................................................. 20
4.1 Subtraction ...................................................................................... 21
4.2 Adaptive thresholding .................................................................... 22
4.3 Connected-component generation .................................................. 22
4.4 Geometric constraints ..................................................................... 23
4.5 Eye verification using support vector machine (SVM) .................. 24
4.5.1 Introduction of support vector machine ............................... 244.5.2 Training data ........................................................................ 26
Chapter 5 Eye Tracking ............................................................................... 28
5.1 Prediction ........................................................................................ 29
5.1.1 Basic concept ....................................................................... 29
5.1.2 Normal update ...................................................................... 30
5.1.3 Tracking-fail update ............................................................. 31
5.2 Two-stage verification .................................................................... 32
Chapter 6 Visual Cue Extraction ................................................................. 34
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6.1 Eye close/open ................................................................................ 34
6.2 Face orientation .............................................................................. 35
Chapter 7 Experiments ................................................................................ 37
7.1 Experimental platform .................................................................... 377.2 Experimental results ....................................................................... 37
7.2.1 Eye detection ........................................................................ 37
7.2.2 Eye tracking ......................................................................... 39
7.2.3 Discussions ........................................................................... 39
Chapter 8 Conclusions and Future Work .................................................... 44
8.1 Conclusions .................................................................................... 44
8.2 Future work .................................................................................... 44
References ................................................................................................... 45
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List of Figures
Fig.1.1. The process steps of the driver drowsiness detection system. ......... 3
Fig.2.1. Eye deformable templates. ............................................................... 6
Fig.2.2. The masks of dimension ( ) ( )1212 mm ++ axax RR that are
convoluted with the gradient image. ............................................... 7
Fig.2.3. Face segmentation. (a) Original color image. (b) Skin
segmentation. (c) Connected components. (d) Best-fit ellipses. ..... 8
Fig.2.4. The first Purkinje image. ................................................................. 9
Fig.2.5. Composition of image capture apparatus. ..................................... 10
Fig.2.6. Difference of shapes around the eyes. ........................................... 10
Fig.2.7. Evaluation criteria for brain waves, blinking, and facial
expression. ..................................................................................... 12
Fig.2.8. Number of eyes close times and alertness level. ........................... 12
Fig.3.1. Principle of bright and dark pupil effects. (a) Bright pupil effect.
(b) Dark pupil effect. ..................................................................... 14
Fig.3.2. Examples of bright/dark pupils (a) Bright pupil image. (b) Dark
pupil image. ................................................................................... 14Fig.3.3.IRlight source configuration. ........................................................ 15
Fig.3.4. The active image acquisition equipment. (a) Front view. (b) Side
view. ............................................................................................... 16
Fig.3.5.IRLEDs control circuit. ................................................................... 17
Fig.3.6. Connection diagram of image acquisition equipment and PC. ..... 17
Fig.3.7. Three different configuration types ofDirectShowfilter. (a)
Recording. (b) Live. (c) Playback. ................................................ 19
Fig.4.1. Steps for eye detection. .................................................................. 20
Fig.4.2. An example of subtraction. (a) The Cframe image (bright pupil
image). (b) TheLframe image (dark pupil image).
(c) The difference image. ............................................................... 21
Fig.4.3. Some examples of positive and negative sets. (a) Positive bright
pupil set. (b) Positive dark pupil set. (c) Negative non-eye set. .... 27
Fig.5.1. The flowchart of two-strategy eye tracking. .................................. 28
Fig.5.2. The concept of prediction and tracking. ........................................ 29Fig.5.3. The ongoing discreteKalman filtercycle. ..................................... 30
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Fig.5.4. A complete diagram of prediction. ................................................. 31
Fig.5.5. The steps of search strategy. .......................................................... 33
Fig.6.1. Eye open/close cue. (a) Image of opened eye. (b) Binary image of
opened eye. (c) Image of closed eye. (d) Binary image of closedeye. ................................................................................................. 35
Fig.6.2. An example of locating the nose position. ..................................... 36
Fig.7.1. Results of eye detection in different light situations. (a) Strong
light. (b) Daytime. (c) Indoor light. (d) Indoor light. (e) Indoor
light. (f) In the Dark. ...................................................................... 38
Fig.7.2. The eye tracking results in a sequence of consecutive frames. The
face is looking downward. ............................................................. 40
Fig.7.3. The eye tracking results in a sequence of consecutive frames. The
face is looking to left. .................................................................... 41
Fig.7.4. The eye tracking results in a sequence of consecutive frames. The
face is looking to right. .................................................................. 42
Fig.7.5. The false detected eye region. ....................................................... 43
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List of Tables
Table 2.1. Technique for Detecting Drowsiness ........................................... 4
Table 7.1. The Detection Rate of Three Different Testing Videos .............. 39
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Chapter 1
Introduction
In this chapter, we describe the motivation of our study, present the
overview of our drowsiness detection system and give the organization of this
thesis.
1.1MotivationIt is a hard endurance for drivers to take a long-distance driving. It is also
difficult for them to pay attention to driving on the entire trip unless they strong
willpower, patience, and persistence. Thus, the driver drowsiness problem has
become an important factor of causing traffic accidents, and then the driver
assist and warning systems were promoted to detect the drivers status
consciousness [6].
We have focus on the development of computer vision techniques extract
the useful visual cues for the detection. The acquired images should give
relatively consistent photometric property under different climatic and
ambient conditions; the images should also produce distinguishable features
to facilitate the subsequent image processing. In this study, we used an IR
camera and blinkingIRilluminator. The use of infrared illuminator has three
purposes: (i) It minimizes the impact of different ambient light conditions,
therefore ensuring image quality under varying real-word conditions
including poor illumination, day, and night tours. (ii) It allows producing the
bright pupil effect, which constitutes the foundation for detecting and tracking
the proposed visual cues. (iii) Infrared is barely visible to the driver; thus, the
IR illumination will minimize any interference with the drivers driving.
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1.2System overviewThe main tasks in the proposed system are to detect, track, extract ad
analyze the drivers eye and head movement for monitoring drivers status of
consciousness.
The proposed system is consisted of four components: an active image
acquisition equipment, eye detection, eye tracking, and visual cue extraction
as shown in Fig.1.1. For working under various light environments, we used
an IR camera equipped with a blinking IR illuminator to acquire derivers
pupils and face. The system is detection and tracking derivers eye for
monitoring drivers state of consciousness. The alternative images are
obtained by the active image acquisition equipment. Then detect and tracking
eye position in alternative images. After obtain the eye position, we extracted
the necessary visual cues such as eyelid movement and head movement to
detect the drowsiness of driver.
1.3Thesis organizationThe remaining sections of this thesis are organized as follows. Chapter 2
is the survey of the related works. In Chapter 3, we introduce the bright and
dark pupil effects. The blinking IR illuminator based on the effects is also
presented in this chapter. Chapter 4 describes the eye detection and
verification methods. Chapter 5 describes the eye tracking method. Chapter 6
presents the eye close/open and face orientation estimation methods. Chapter
7 is reports experiments and the results. Chapter 8 is the conclusions and
some suggestions for future works.
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Fig.1.1. The process steps of the driver drowsiness detection system.
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Chapter 2
Related Works
In this chapter several related works to our study are surveyed.
2.1Techniques for detecting drowsinessUeno et al.[23] presented possible techniques for detecting drowsiness
of drivers that can be broadly divided into five major categories as shown inTable 2.1.
Table 2.1. Technique for Detecting Drowsiness
Detection techniques Description
Physiological
signals
Detection by changes in brain waves,
blinking, heart rate, skin electric potential,
etc.Sensing of
human
physiological
phenomenaPhysical
reactions
Detection by changes in inclination drivers
head, sagging posture, frequency at which
eyes close, grapping force on steering
wheel, etc.
Sensing of driving operation
Detection by changes in driving operations
(steering, accelerator, braking, shift lever,
etc.)
Sensing of vehicle behaviorDetection by changes in vehicle behavior(speed, lateral G, yaw rate, lateral position,
etc.)
Response of driver Detection by periodic request for response
Traveling conditions
Detection by measurement of traveling time
and conditions (day hour, nigh hour, speed,
etc.)
Among these methods, the best one is based on the physiological
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phenomena and which can be accomplished by two ways. One way is to
measure the changes of physiological signals, such as brain waves, eye
blinking, heart rate, pulse rate, and skin electric potential, as means of
detecting a drowsy situation. The approach is suitable for making accurate
and quantitative judgments of alertness levels; however, it must annoy drivers
to attach the sensing electrodes on the body directly. Thus, it would be
difficult to use based on the sensors under real-world driving condition. The
approach has also the disadvantage of being ill-suited to measure over a long
period of time owing to the large effect of perspiration to the sensors.
The other way focuses on the physical changes, such as the inclination of
the drivers head, sagging posture, and decline in gripping force on steering
wheel or the open/closed state of the eyes. The measurements of these
physical changes are classified into the contact and the non-contact types. The
contact type involves the detection of movement by direct means, such as
using a hat or eye glasses or attaching sensors to the drivers body. The
non-contact type uses optical sensors or video cameras to detect the changes.
2.2Existed drowsiness detection systemsMany active systems [5-7, 10-12, 15, 16, 19, 20, 23, 25] have been
proposed for detecting driver drowsiness to avoid traffic accidents. Typical
drossiness detection systems are generally classified into drivers feature
detection and drowsiness judgment.
2.2.1Drivers feature detectionThe primary task of drivers feature detection is to detect drivers face
and eyes through the techniques of image processing and compute vision.
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Yuille et al. [26] presented a method for detecting and describing eye
features using deformable templates as shown Fig.2.1. The eye feature is
described by a parameterized template and an energy function is used to
define the links of edges, peaks, and valleys in the image intensity to the
corresponding properties of the template. The template then interacts
dynamically with the image, by altering its parameter values to minimize the
energy function, thereby deforming itself to find the best fit.
Fig.2.1. Eye deformable templates.
Bakic and Stockman [1] used a skin color model (normalized RGB)
within a connected components algorithm to extract a face region. The eyes
and nose is found based on the knowledge of the face geometry. The eye blob
is found by gradual thresholding smoothed red component intensity. For eachthresholded image, the connected components algorithm is used to find dark
blobs. Each two blobs that are candidates for the eyes are matched to find the
nose. To find the nose, the image of the red component intensity is
thresholded at average intensity. Frames are processed in sequence and a
Kalman filter is used to smooth the feature point coordinates over time. Next
frame is then predicted; if the predictions are verified, the initial frame
processing is passed; if predictions are not verified, the entire process
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described above is repeated.
DDorzio et al.[3] presented an approach based on the phenomenon that
iris is always darker than the sclera no matter what color it is. In this way, theedge of the iris is relatively easy to detect. Then a circle detection operator
based on the directional Circular Hough Transform is used to locate the iris. A
range [Rmin, Rmax] is set to tackle different iris dimensions. In this algorithm is
based on convolutions applied to the edge image. The masks shown in Fig.2.2
represent in each point the direction of the radial vector scaled by the distance
form the center in a ring with minimum radiusRmin, and maximum radiusRmax.
The circle detection operator is applied on the whole image without any
constraint on plain background or limitations on eye regions. Search
maximum value M1 of the output convolution in the whole image is best
candidate to contain an eye. Search the second maximum value M2 in the
region that is candidate to contain the second eye. Then verify whether M1
andM2are eyes pair or not.
Fig.2.2. The masks of dimension ( ) ( )1212 mm ++ axax RR that are
convoluted with the gradient image.
Sobottka and Pitas [20] presented an approach for face localization based
on the face oval shape and skin color, as one example shown in Fig.2.3.
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(a) (b)
(c) (d)
Fig.2.3. Face segmentation. (a) Original color image. (b) Skin segmentation.
(c) Connected components. (d) Best-fit ellipses.
Huang and Marianu [9] presented a method to detect human face and
eyes. They used multi-scale filters to get the pre-attentive features of objects.
Then these features are supplied to three different models to analyze the
image further. The first is a structural model that partitions the features into
facial candidates. After they obtain a geometric structure that fits theirconstraints they use affine transformations to fit the real world face. Secondly,
they used a texture model to measure color similarity of a candidate with the
face model, which includes variation between facial regions, symmetry of the
face, and color similarity between regions of the face. The texture comparison
relies on the cheek regions. Finally they used a feature model to obtain the
candidate position of the eyes, and they used eigen-eyes combined with image
feature analysis for eyes detection. Then they zoom in on the eye region and
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perform more detailed analysis. Their analysis includes Hough transforms to
find circles and reciprocal operations using contour correlation.
Shih et al. [19] presented a system using 3-D vision techniques toestimate and track the 3-D sight line of a person. The approach uses multiple
cameras and multiple point light sources to estimate the line of sight without
using user dependent parameters, thus avoiding cumbersome calibration
processes. The method uses a simplified eye model, and it first uses the
Purkinje images of an infrared light source to determine eye location. When
light hits a medium part is reflected and part is refracted. The first Purkinje
image is the light reflected by the exterior cornea as shown in Fig.2.4 [11].
Then they use linear constraints to determine the line of sight, based on their
estimation of the cornea center.
Fig.2.4. The first Purkinje image.
Hamada et al. [5] presented a capture system and image processing to
solve in measuring the blinking of a driver during driving. First, they have
developed a capture method against changes in the surrounding illumination,
LensCornea
Image Plane
OOptical Center
Optical Axis
The 1st Purkinje Image
Pims
Q
Point Light Source (LED)
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as shown in Fig.2.5. Then an image processing deals with the difference in the
shapes of faces and the shapes around the eyes of individual people, as shown
in Fig.2.6. It deals with the difference in blinking waveforms that differs from
individual to individual. Finally, it presumes the drivers consciousness from
the changing blinking period.
Fig.2.5. Composition of image capture apparatus.
Fig.2.6. Difference of shapes around the eyes.
Shadow by swelling eyeball
Wrinkle and shadow
Shadow by swelling of eyeballWrinkle and shadow
Polarizing filterInfrared light
Controller
CCD camera
IR pass filterPolarizing filter
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2.2.2Drowsiness judgmentThe primary purpose of drowsiness judgment is to judge drivers
drowsiness situation by different cues.
Ueno et al.[23] estimated drivers alertness level by detecting number of
eyes close, and they verified that using brain wave (wave). They devise an
alertness index for quantitative judgments of drivers situation of drowsiness.
This index is based on the assignment of points to brain waves, blinking and
facial expression. The point total provides a quantitative measure for judging
the alertness level. The specific procedure for rating these three elements is
shown in Fig.2.7. As a persons level of alertness drops, a large number of 2
waves appear and their amplitude becomes larger. Blinking is rated by
evaluating the measured waveforms for the upper and lower electric potential
of the eyes. In a normal state of alertness, blinking appears as sharp spikes in
the waveform. As the level of alertness drops, the spikes appear more
frequently and subsequently lose their shape to become a gentle waveform
when a person becomes drowsy. Eventually, the waveform shows trapezoidal
shapes indicating that the eyes close for long interval. Fig.2.8 presents
experimental results showing the alertness level and the number of times the
driver eyes closed for two or more seconds while driving on a test course.
This result indicated that a reduced level of alertness could be detected with
good accuracy by monitoring changes in the degree of openness of thedrivers eyes.
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Fig.2.7. Evaluation criteria for brain waves, blinking, and facial expression.
Fig.2.8. Number of eyes close times and alertness level.
Smith et al. [18, 19] presented a system not only for detecting
drowsiness of driver but also for analyzing human driver visual attention. The
system relies on estimation of global motion and color statistics to track a
persons head and facial features. The system classifies rotation in all viewing
directions, detects eye/mouth occlusion, detects eye blinking and eye closure,
and recovers the three dimensional gaze of the eyes to determining drivers
visual attention by a hierarchical detecting and tracking method.
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Chapter 3
An Active Image Acquisition Equipment
The proposed eye detecting and tracking methods are based on the
special bright and dark pupil effects. In this chapter we explain what bright
and dark pupil effects are. We obtained bright and dark pupil effects image by
our active image acquisition equipment that consists of an IR camera and a
two rings infrared illuminator. We illustrate the configuration. And then
describe our hardware and software architectures.
3.1Introduction of bright/dark pupil phenomenonAccording to the original patent from Hutchinson [10], a bright pupil can
be obtained if the eyes are illuminated with a NIRilluminator beaming light
along the camera optical axis at certain wavelength. At the NIRwavelength,
pupils reflect almost all IR light they receive along the path back to the
camera, producing the bright pupil effect, very much similar to the red eye
effect in photography. If illuminated off the camera optical axis, the pupils
appear dark since the reflected light will not enter the camera lens. This
produces the so-called dark pupil effects. Fig.3.1 is illustrated the principle of
bright and dark pupil effects. An example of the bright and dark pupil image
is shown in Fig.3.2.
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(a)
(b)
Fig.3.1. Principle of bright and dark pupil effects. (a) Bright pupil effect. (b)
Dark pupil effect.
(a) (b)
Fig.3.2. Examples of bright/dark pupils (a) Bright pupil image. (b) Dark pupil
image.
IR Light source
Incident infrared light
Reflected infrared light
Incident infrared light
Reflected infrared light
IR light source
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3.2Two-ringIRilluminatorWe used a simple geometric disposition of theIRLEDs similar to that of
morimoto et. al. [24] that can achieve bring and dark pupil effects but withminimal reduction in camera operational view. This IR illuminator consist
two sets of IR LEDs, distributed evenly and symmetrically along the
circumference of two coplanar concentric rings as shown in Fig.3.3. We used
a USB infrared camera acquisition bright and dark pupil images. Mounting
our two-ring IR illuminator in front of the camera, a dark pupil image is
produced if the outer ring is turned on and a bright pupil image is produced if
the inner ring is turned on. A physical set-up of theIRilluminator is shown in
Fig.3.4.
Fig.3.3.IRlight source configuration.
Camer
Inner ringIR LEDsOuter ringIR LEDs
IRilluminator
USB IRcamera
Front View Side view
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(a) (b)
Fig.3.4. The active image acquisition equipment. (a) Front view. (b) Side
view.
The IR illuminator control circuit is shown in Fig.3.5. Inner and outer
rings are individuallyused a transistor that connect toRS-232 to control turn
on or turn off. The control transistor of inner ring connects to DTRpin, and
another rings transistor connects to RTS pin. The ring is turned on if itsvoltage of the control pin is set high; otherwise, the ring is turned off. Turn on
and off is synchronized with the image capture. The Connection diagram of
image acquisition equipment andPCis shown in Fig.3.6.
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0913
330
1K
5V
RS-232
(DTR) Pin 4
(RTS) Pin 7
330
Fig.3.5.IRLEDs control circuit.
Fig.3.6. Connection diagram of image acquisition equipment and PC.
Camera
Innersignal
Outer
signal
USB
RS-232
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3.3Hardware and software architecturesFor synchronization inner and outer LEDs with every frame of the
alternative image, We have developed a program to synchronize the outer ringofLEDs and inner ring ofLEDs with the even and odd frame of the alternative
image respectively so that can be turned on and off alternatively. We utilized
Microsoft Foundational Classes (MFC) library and DirectShow SDK to
develop our USBCamera capture andIRswitch control program. The building
block of DirectShow is a software component called a filter. A filter is a
software component that performs some operation on a multimedia stream. For
example, DirectShow filters can read files, get video from a video capture
device, decode various stream formats, such as MPEG-1 video, and pass data to
the graphics or sound card. InDirectShow, an application performs any task by
connecting chains of filters together. In our program used three different
configuration type of DirectShow filter: recording, playback and living, as
shown in Fig.3.7. Recording type is used to record video to file. Living type is
used to perform eye detection and tracking in real-time. Playback type can
play the video file that is recorded by recording type frame by frame. It can
help us to develop our eye detection and tracking algorithm.
In these types, a specialDirectShowfilter is Sample Grabber filter. It is a
transform filter that can be used to grab media samples from a stream as they
pass through the filter. It provides a way to retrieve samples as they pass
through the filter graph. When a sample is retrieved, it notifies us. Therefore
we can process this sample and switch inner and outer ringLEDs.
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Capture
source filterSample
grabber
AVI
Mux
AVI
fIle
IR switch control
(a)
Capture
source filter
Sample
grabber
AVI
decompressor
Video
render
Eye detection/tracking
&
IRswitch control
(b)
File sourcefilter Samplegrabber AVIdecompressor Videorender
Eye detection/tracking
(c)
Fig.3.7. Three different configuration types ofDirectShowfilter. (a)
Recording. (b) Live. (c) Playback.
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Chapter 4
Eye Detection
In this chapter we described procedure of eye detection as shown in
Fig.4.1. All function will be described in the following sections, respectively.
1
2
Subtraction
Adaptive
Thresholding
Connected-component
generation
Geometric constraints
Eye verification
using SVM
Alternative
images
CframeL frame
difference image
binary image
blobs
eye candidates
Eyes
Fig.4.1. Steps for eye detection.
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4.1SubtractionThe C frame is current obtained frame and the L frame is the stored
frame in the last time. If Cframe is bright pupil image, then L fame is dark
pupil image, and vice versa. While both images share the same background
and external illumination, pupils in the bright pupil image look significantly
brighter than in the dark pupil image, as shown in Fig.4.2. To eliminate the
background and reduce external light illumination, the Cframe is subtracted
from theLframe producing the difference image as shown in Fig.4.2 (c), in
which most of the background and external illumination effects are removed.
(a) (b)
(c)
Fig.4.2. An example of subtraction. (a) The Cframe image (bright pupil
image). (b) TheLframe image (dark pupil image). (c) The difference
image.
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4.2Adaptive thresholdingAfter a subtraction, we can obtain a difference image. The difference
image is a gray-level image and will be thresholded to extract pupils. The
pupils region in the difference image are bright than the background; pixels
with gray level less than threshold value Tdf are labeled black; otherwise,
labeled white. Thus the pupil pixels are white in the binary image.
The adaptive thresholding algorithm is presented as follows.
Step 1. Select an initial threshold value T; usually it is set 128.
Step 2. Threshold the image with threshold value T. This will produce two
groups of pixels: G1, consisting of all pixels with intensity values >= T,
and G2, consisting of pixels with values < T.
Step 3. Compute the average intensity values 1 and 2 for the pixels in
regions G1and G2.
Step 4. Compute a new threshold value:
Step 5. ( )2121 +=T .
Step 6. Repeat Steps 2 through 4 until the difference whit Tin successive
iterations is smaller than a predefined parameter T0.
Step 7. Finally we obtained a threshold value T. we adjust this value to suit
the proposed system. If (T>= 40) {Tdf= 255 T 40} else {Tdf= 40}.
4.3Connected-component generationAfter adaptive thresholding, we obtained a binary image. The next task is
to apply connected components algorithm, label each bright pixel, and find
the center, size and boundary box of each blob.
Let I be a binary image and let F and B be the foreground and
background pixel subsets inI, respectively. A connected component ofI, here
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referred to as Cis a subset ofFof maximal size such that all the pixels in C
are connected. Two pixels, p and q are connected if there exists a path of
pixels (p0, p1, , pn) such that p0= p, pn= q and ni1 , pi-1and piare
neighbors. Here, the definition of connected component relies on that of a
pixels neighborhood: if all paths between pixels in Care 4 connected, then C
is an 4-connected component. The classical sequential algorithm for labeling
connected components consists of two subsequent raster-scans ofI. In the first
scan a temporary label is assigned to each pixelFbased on the values of its
neighbors already visited by the scan. For 4-neighbor connected components,the pre-visited neighbors are upper and left neighbor pixels.
As a result of the scan, no temporary label is assigned to pixels
belonging to different components but different labels may be associated with
the same component. Therefore, after completion of the first scan equivalent
labels are sorted into equivalence classes and a unique class identifier is
assigned to each class. Then a second scan is run over the image so as to
replace each temporary label by the class identifier of its equivalence class.
The classical sequential method is not efficient enough. So we implemented a
simple and efficient 4-connected components labeling algorithm [21].
4.4Geometric constraintsAfter connected-component generation, we obtained many binary
candidate blobs. Pupils are found somewhere in these candidates. However, it
is usually not possible to isolate eye blob only by picking the right threshold
value, since pupils are often small and not bright enough compared with other
noise blobs. We can distinguish pupils blobs with other noise blobs by their
geometric shapes. In our system, we defined several constraints to make the
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distinction as follow:
(i) 1 < blob size < 60,
(ii) 1 < Width of blob bounding box < 20,
(iii) 1 < Height of blob bounding box < 20, and
(iv) (Bounding box size - blob size) < 10.
4.5Eye verification using support vector machine (SVM)With the proposed geometric constraints, several non-pupil blobs may
leave because they are similar the pupil blob in shape and size and we cant
distinguish the real pupil from them. So we need another feature to separate
pupil and non-pupil blobs. Here we use Support Vector Machine [24]
classification to verify whether each candidate blob is an eye or not.
4.5.1Introduction of support vector machineFor the case of two-class pattern recognition, the task of predictive
learning from examples can be formulated as follows [24]. Given a set of
functions
{ } { }1,1:,: + NRff (4.1)
(is an index set) and a set of examples
( ) ( ) ( ) { }1,1,,,,,,,,, 11 + iN
illii yRxyxyxyx LL , (4.2)
each one generated from an unknown probability distribution P(x,y), one
wants to find a functionf* which provides the smallest possible value for the
risk
( ) ( ) ( )yxdPyxfR , =
. (4.3)
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The SVMimplementation seeks separating hyperplanesD(x) defined as
( ) ( ) 0wxwxD += (4.4)
by mapping the input data x into a higher dimensional space z using a
nonlinear functiong. Low weights wdefining the class boundaries imply low
VC-dimension and lead to high separability between the class patterns. An
optimal hyperplane has maximal margin. The data points at the (maximum)
margin are called the support vectors since they alone define the optimal
hyperplane.
The reason for mapping the input into a higher dimension space is that
this mapping leads to better class separability. The complexity of SVM
decision boundary, however, is independent of the feature z space
dimensionality, which can be very large (or even infinite).SVMoptimization
takes advantage of the fact that the evaluation of the inner products between
the feature vectors in a high dimensional feature space is done indirectly via
the evaluation of the kernel H between support vectors and vectors in theinput space
( ) ( )xxzz = H , (4.5)
where the vectors z and z are the vectors x and x mapped into the feature
space. In the dual form, the SVM decision function has the form
( ) ( )xxx
== ,1 iM
iii HyD . (4.6)
TheRBFs kernelsHare given by
( )
=2
2
exp,
iiH
xxxx , (4.7)
and the corresponding SVMhyperplanes are defined then as
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( )
+
= =
M
i
ii bsignf
12
2
exp
xx
x , (4.8)
and can be fully specified using dual quadratic optimization in terms of the
number of kernels usedMand their width.
The polynomial kernelsHof degree qare given by
( )[ ]qH 1),( += xxxx , (4.9)
and the corresponding SVMhyperplanes are defined as
( ) ( )[ ]
++=
=
M
i
qi bsignf
1
1xxx . (4.10)
4.5.2Training dataTraining data are needed to obtain the optimal hyper-plane. The size of
image we use 3020 pixels and the image pixel are processed using histogram
equalization and normalized to [0, 1] range before training. The eye training
images were divided into three set: positive bright pupil set, positive dark
pupil set, and negative set. In the whole positive image set, we include eye
images of different gazes, different degrees of opening, different subjects, and
with/without glasses. The non-eye images were placed in the negative image
set. Some examples of eye and non-eye images in the training sets, as shown
in Fig.4.3. SVMcan work under different illumination conditions due to the
intensity normalization for the training images via histogram equalization.
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(a)
(b)
(c)
Fig.4.3. Some examples of positive and negative sets. (a) Positive bright pupilset. (b) Positive dark pupil set. (c) Negative non-eye set.
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Chapter 5
Eye Tracking
In this chapter we present our tracking method. If eye detection is
succeed in consecutive three frames, the procedure is turned to tracking phase.
There are two strategies in the tracking phase. First strategy is eye detection in
a predicted region. If eye detection in the predicted region is fail. The second
strategy is then used. We search the center of eye darkest in predicted region.
The detailed steps are shown in Fig.5.1 and described in the following
sections.
Fig.5.1. The flowchart of two-strategy eye tracking.
Eye detection
Success?
no
1st& 2nd success
yes
no
yes
Consecutive
three frames
Eye detection in
predicted region
Success?
Search darkest
eyeball center in
predicted region
Success?
Update the
predicted parameter
(normal update)
tracking phase
1st& 2nd failno
yes
no
Tracking fail
update
Initial thepredicted
parameter
detection phase
Alternative
images
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5.1PredictionThe concept of prediction and tracking is described in Fig.5.2; in which
the position at time t+1 is predicted form the position and velocity of pupil
blob at times t, t-1, and t-2.
Fig.5.2. The concept of prediction and tracking.
5.1.1Basic conceptTheKalman filteris the most popular method to estimate the position of
moving objects. Here we used Kalman filter concept to predict the new
position of pupil.
In 1960, R.E. Kalman [26] published his famous paper describing a
recursive solution to the discrete-data linear filtering problem, and then the
Kalman filter has been the subject of extensive research and application,
particularly in the area of autonomous or assisted navigation. The Kalman
filteraddresses the general problem of trying to estimate the state nR of a
discrete-time controlled process with an observationm
z . We can use the
(xt+1,yt+1)
(xt,yt) Detected
position at
time t
Predicted positionand search area at
time t+1
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models and observations in two ways. First, multiple observations z1, z2,
should permit an improved estimate of the underlying model x. Second, the
estimate ofxat time kmay also provide a prediction for the observationxk+1,
and thereby forzk+1. Whereby we observezk, estimatexk, predictxk+1thereby
predict zk+1,observezk+1taking advantage of the prediction, and then update
our estimate of xk+1. A predictor-corrector algorithm for solving numerical
problems is shown in Fig.5.3.
Fig.5.3. The ongoing discreteKalman filtercycle.
5.1.2Normal updateWe here use the Kalman filter concept to predict the new positions of
pupils. Let (xt, yt) represent the pupil center pixel at time tand (ut, vt) be its
velocity at time tinxandydirections, respectively.The predicted position of
pupils can be calculated by
tuxx ttt +=+1 (5.1)
and
tvyy ttt +=+1 , (5.2)
where tmeans the interval time between two frames.
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Then we use the correct position of the pupil to update its velocity,
( ) ttt suu += 11 , (5.3)
( ) ttt zvv += 11 , (5.4)
( ) txxs ttt /1= , and (5.5)
( ) tyyz ttt /1= , (5.6)
where (st, zt) is the correct velocity of the pupil in time t, and , is the
weight number to correct the (ut, vt). The complete diagram of predict
operation as shown in Fig.5.4.
tvyy ttt +=+1
tuxx ttt +=+1
Fig.5.4. A complete diagram of prediction.
5.1.3Tracking-fail updateThe predicted region is based on the predicted position. Its width and
height are invariant. Sometimes, our two strategies are failed because pupil is
covered briefly by something. If our two strategies are failed in tracking phase
at time t, equations
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111 2 + += ttt uxx (5.7)
and
111 2 + += ttt vyy , (5.8)
are used to update predicted parameters.
If tracking is success in the next frame, we normally update the predicted
parameter; otherwise tracking-fail update is done again. If the tracking failed
in consecutive three frames, the procedure is turned to detection phase, and the
predicted parameters will be clear and reinitialize.
5.2Two-stage verificationAfter the prediction, we detect eye in the predicted region. The eye
detection method is the same globe eye detection, only a difference is
operator in a small region. It can avoid much unnecessary detection and save
up much detection time. If the detection is fail, the searching stage is
launched.
If the SVM was not well trained or the pupils are not as bright due to
either face orientation or external illumination interference, the detection may
fails. Then the searching strategy is launched to search the eyeball center. The
steps of the searching is described in Fig.5.5. In this strategy, we clip the
predicted region form the current frame, and make a binary image from the
clipped region with a threshold value Tdk. The threshold value is made by
adaptive thresholding algorithm. Pixels with gray level greater than Tdk are
labeled black; otherwise pixels are labeled white. Then employ connected
component algorithms to find the center and boundary box of each blob. We
find the largest size of blob and used its center as the fined eye center. We
defined some constraints for to verify the fined eye position. The constraints
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are shown as:
(i) 20 < blob size < 400.
(ii) The distance between this position and position of last frame < 10.
Adaptive thresholding
noyes Verify
success?
Connect-component generation
Search the center of the largest blob
Predicted eye
region image
Binary image
Blobs
The center of eye Fail
Fig.5.5. The steps of search strategy.
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Chapter 6
Visual Cue Extraction
In this chapter, we explain several methods to extract visual cues for
drowsiness detection the visual cues include eye close/open and face
orientation.
6.1Eye close/openAfter we obtained the positions of eyes, we determinant the eyes are
opened or closed. At first, a binary image is generated from the eye region by
the adaptive thresholding algorithm. Then, pixels which gray levels are larger
than Teware labeled as white and pixels which gray levels are less than Teware
labeled as black. The result can be expressed as
( ) ( )( ) ( )
=>=
ew
ew
Tyxgyxg
Tyxgyxg
,if,0,
,if,1, , (6.1)
whereg(x,y) is the gray level of pixel (x,y). We accumulate the white pixels
in the eye region,
= =
=M
x
N
y
yxgn1 1
),( , (6.2)
whereMis width of the eye region, Nis height of eye region. We defined a
threshold value Tcp. If n is greater than Tcp, we classify the eye into case of eye
closing. If nis less than Tcp, we classify the eye into case of eye opening as
examples shown in Fig.6.1.
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(a) (b)
(c) (d)
Fig.6.1. Eye open/close cue. (a) Image of opened eye. (b) Binary image of
opened eye. (c) Image of closed eye. (d) Binary image of closed eye.
6.2Face orientationThe face orientation is also an important visual cue for detecting
drowsiness. When the driver does not pay attention to forward direction for a
long time, system should give a warning to driver. In this study, we used the
positions of two eyes and nose to estimate the face orientation.
After extracting two eyes, we utilize their positions to locate the position
of nose. At first, we use the distance between two eye region centers to definesquare searching area as shown in Fig.6.2. The area is just below the eyes
with side length as the mentioned distance. Secondly, in the area we
accumulate the gray levels in both horizontal and vertical to fit the horizontal
and vertical accumulation curve.
We search along the horizontal accumulation curve and find the first
valley point to be the y coordinates of nostrils, and we search along the
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vertical accumulation curve and find average of two valley points to be the x
coordinates of the median of two nostrils.
Fig.6.2. An example of locating the nose position.
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Chapter 7
Experiments
Several experiments and comparisons are reported in this chapter. At first,
we introduce our develop platform. Secondly, we demonstrate several
detection and tracking results.
7.1Experimental platformAll algorithms were implemented with C++ programming language,
Microsoft Foundational Classes (MFC) Library, and DirectShow SDK. All
experiments were executed on a general PCwith AMD AthlonTM
2500+
CPU
and Microsoft Windows XP professional operation system.
7.2Experimental resultsAll experimental image sequences were recorded by our active image
acquisition equipment. The frame sizes are all 320240 pixels with 30
frame/sec rate.
7.2.1Eye detectionThe light conditions: strong light, normal light, and dark, were
considered to evaluate the performance of the eye detector. Six results are
shown in Fig 7.1 and we can see that the detector can work correctly in
different ambient light conditions.
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(a) (b)
(c) (d)
(e) (f)
Fig.7.1. Results of eye detection in different light situations. (a) Strong light.
(b) Daytime. (c) Indoor light. (d) Indoor light. (e) Indoor light. (f) In
the Dark.
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7.2.2Eye trackingThree videos were used to evaluate the eye tracker as shown in
Figs.7.2-7.4. When the tester tuned his head, our tracker can continuously
track their eye positions.
7.2.3DiscussionsIn the videos, the proposed detector can all detected two regions, each
contains an eye; but sometimes the detected center of eye region is not the
actual eye as one example is shown in Fig.7.5. In Fig.7.5, the two eye regions
are marked a blue rectangle, but the detected positions two eyes (the center of
eye) are not correct.
We checked all detected frames to count the detection rate as listed in
Table 7.1. In the table, B means the false detection number in bright pupil
frame, and D is the false detection number in dark pupil frame.
Table 7.1. The Detection Rate of Three Different Testing Videos
Detection
errorVideo eyeTotal
frameCorrect
B D
Total
error
Detection
Rate
left 124 12 21 33 78.9%
V1 right 157 127 9 21 30 80.8%
left 110 0 20 20 84.6%V2
right130
121 2 7 9 93.0%
left 369 4 6 10 97.3%V3
right379
374 2 3 5 98.6%
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(a) (b)
(c) (d)
(e) (f)
Fig.7.2. The eye tracking results in a sequence of consecutive frames. The
face is looking downward.
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(a) (b)
(c) (d)
(e) (f)
Fig.7.3. The eye tracking results in a sequence of consecutive frames. The
face is looking to left.
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(a) (b)
(c) (d)
(e) (f)
Fig.7.4. The eye tracking results in a sequence of consecutive frames. The
face is looking to right.
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Fig.7.5. The false detected eye region.
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Chapter 8
Conclusions and Future Work
In this chapter, we give conclusions and suggestions for future work.
8.1 ConclusionsIn this study, we developed a prototype computer vision system with
active image acquisition equipment for drowsiness detection of driver. It is
detection, tracking the drivers eye in alternative images that are obtained by
our active Image acquisition equipment. Therefore some visual cues can be
extracting form the detected eyes. It can detect eye positions in the different
ambient light conditions.
8.2 Future workSeveral problems should improve in the future. First, The SVM eye
verification is time consuming. Second, if the drivers head move too fast, the
tracker can not tracking accurately. We will improve the tracking method.
Thread, the extracted visual cues are not enough to detecting drivers status.
We can add more visual cues such as gaze direction, facial expression, etc.
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