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Drivers Eye State Identification Based on Robust Iris Pair Localization*Tauseef Ali, **Khalil Ullah*Myongji univ.,deptt. Electronics and comn. Engg. (TEL: 010-5814-1333;E-mail: tuaseefcse@yahoo.com )
**Myongji univ.,deptt. Electronics and comn. Engg. (TEL: 010-8691-8402;E-mail:khalil_dirvi@yahoo.com)
Abstract In this paper, we propose a novel and robust approach to determine eye state. The method is based on
robust iris pair localization. After iris pair is detected from image, it is analyzed by comparing its openness with
the normal images of the person. Our approach has five steps: 1) Face detection 2) Eye candidate detection 3)
Tuning candidate points 4) Iris pair selection and 5) Eye Analysis. Experimental results for iris pair localization
and eye state identification are shown separately. For testing three public image databases, Yale, BioID and
Bern are used. Extensive experiments have shown the effectiveness and robustness of the proposed method.
Keywords Iris pairs, Eye candidate, Eye analysis, Eye state
1.IntroductionMonitoring a drivers visual attention is very important for
detecting fatigue, lack of sleep, and drowsiness. By
automatically detecting eye state and drowsiness level of driver,
an alarm can be activated to inform driver or other authority
which can avoid a large number of road accidents. Robust eye
detection is crucial step for this kind of application. After robust
eye detection, information about the gaze, eye blinking, and
drowsiness can be determined. Some work has been done on this
subject but the problem is still far from being fully solved. Some
algorithms for eye detection have obtained good results such as
[1] but they cant points out exact center of iris which can further
be used for drowsiness detection. Generally eye detection is
achieved using active or passive techniques. Active techniques
are based on spectral characteristics of eye under IR
illumination.[2]. These techniques are very simple and give good
results but the success rates of such systems require stable
lighting conditions and person close to camera. Passive
techniques locate eyes based on their different shape and
appearance from face. In these techniques, generally, first face is
detected to extract eye regions and then eyes are localized using
eye windows. Much work has been done on face detection and
there are robust algorithms available [3]. However robust and
precise eye detection is still an open problem. After eye detection,
several measures can be used to determine eye state and detect
drowsiness. Many efforts have been made to detect drowsiness
among drivers [4,5]. Eye blinking is a good measure of detecting
the level of drowsiness. PERCOLS (the percentage of time that
an eye is closed time in a given period) is one of the best
methods to measure the eye blinking as high PERCOLS scores
are strongly related to [6]. However, in this paper, we use a still
image and first detect the centers of eyes and then determine eyestate by comparing the eyes openness in the test image with that
of original image taken when subject is normal or alert. This
kind of system can be used with a camera which input a still
image periodically to the system and the system determine the
eye state in real time and if subject eyes state is close for more
than a few input samples, an alert can be activated to show that
driver is drowsy.
2.Outline Of Proposed MethodThe algorithm detects the face in an input image using
AdaBoost algorithm. By changing the training data and
increasing the false positive rate of the AdaBoost algorithm, we
detect the candidate points for the irises. The candidate points
produced by AdaBoost are tuned such that two of the candidate
points are exactly in the center of iris. Mean crossing function
and convolution template are used to select irises of both eyes
from the tuned candidate points. After locating iris pair, eyes are
analyzed to determine their state. Fig.1 shows the steps in
localizing iris centers of eyes.
Fig.1: steps of the proposed algorithm using a test image.
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3.Face DetectionWe first detect the face in the input image. Then problem is
simplified due to the background being restricted to the face. It
saves searching time and improves accuracy. For face detection,
Violas method [3] is used. A robust face classifier is obtained by
supervised AdaBoost learning. Given a sample set of training
data {xi, yi}, the AdaBoost algorithm selects a set of weak
classifiers {hj(x)} from a set of Haar-like rectangle features and
combine them into a strong classifier. The strong classifier g(x) is
defined as follow:
( )( )
= =otherwise
xhxg
k
k
kk
0
1max
1
(1)
where is the threshold that is adjusted to meet the detection
rate goal. The Haar-like rectangle features are easily computed
using integral image representation. The cascade method
quickly filters out non-face image areas. More details can befound in [3].
4.Eye Candidate DetectionBy changing the training data and increasing the false-positive
rate of the algorithm in section 3, we build an eye candidate
detector. The training data of [7] is used to detect several eye
candidate points in face region. A total of 7000 eye samples are
used with the eye center being the center of the image and resize
to 16*8 pixels. Because in this step face region is already
detected, so the negative samples are taken only from the face
images. We set low threshold and accept more false positive. Onthe average, we get 15 eye candidates out of the detector.
5.Tuning Candidate PointsWe shift the candidate points within a small size of
neighborhood so that two of the candidate points are exactly in
center of irises. The separability filter proposed by Fukui and
Yamaguchi [8] is utilized in an efficient way to shift the
candidate points within a small size of neighborhood. By using
the template in Fig.2, the separability value () is computed for
each point in the neighborhood by the following equation.
AB=
( )( )=
=N
i
miiPyxIA
1
2
,
( ) ( 2222
11 mm PPnPPnB += )
}UL RR ,...
(2)
where nk(k = 1; 2) is the number of pixels in Rk; N = n1+n2; Pk
(k=1; 2) is the average intensity in Rk; Pm is the average intensity
in the union of R1 and R2, and I (xi; yi) the intensity values of
pixels (xi; yi) in the union of R1 and R2.
Separability values for each of the point in the neighborhood
are determined by varying the radius in a range{ }. The
point in the neighborhood which gives maximum separability is
considered as the new candidate point. We also find the
separability values for each new candidate point and its
corresponding optimal radius R among { [9]. Theseseperability and radius values for new candidate points are
used later. Fig 1(d) shows the tuned candidate points.
UL RR ,...
Fig . 2 An eye template (R1 is the inside region of the smaller
circle and R2 is the region between the two concentric circles).
6.Iris Pair SelectionWe combine three metrics to measure the fitness of
each candidate point with iris and select iris pair.
Mean crossing function
A rectangular subregion is formed around each iris candidate.
The size of the subregion is depicted in Fig. 3, where R is the
radius of the candidate determined in section 5.
( )ji,
Fig. 3: Subregion for mean crossing function
A subregion of the form shown in Fig. 3 is formed around each
candidate point. The subregion is scanned horizontally and the
mean crossing function [10] for pixel is computed as
follows:
( ) ( )( ) ( )
++
++
=
otherwise
AjiIifthenjiIIf
AjiIifthenjiIIf
jiC
;0
1,A,;1
1,A,;1
),(
= =
=M
i
N
j
subregion jiCC
1 1
),(
(3)
where A is a constant. The horizontal mean crossing value for
the subregion is determined as
(4)In a similar way, vertical mean crossing function is
evaluated by scanning vertically the subregion. To find the
final mean crossing value for the subregion, we linearly
add both mean crossing numbers.Convolution with edge image subregion
First we find the edge image of the subregion around the
candidate point. The size of the subregion is the same as the
mask in Fig. 4. The subregion is convolved with the convolution
kernel shown in Fig. 4 with the edge image of the subregion.
The radius of the template is equal to the radius of the candidate
determined in section 5. The center of the template is placed on
the candidate point and the value of convolution is determined.
The process is repeated for each of the candidate. The resultant
signal from convolution is summed up and a single value is
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.2 Eye State Identification
es of the same subject with varying
am
8
Fig. 9 and 10 shows imag
ount of eye closure. The detected radius and the state
classified by the algorithm are shown. For both subjects the face
image determined in section 3 is in the range of 140 X 140 to160 X 160 and threshold chosen is 3.
(a) (b) (c)
Fi ified as
Clas
g. 9: (a) Detected Iris Radius by algorithm = 4, Class
Open Eyes. (b) Detected Iris Radius by algorithm = 3,
sified Open Eyes. (c)Detected Iris Radius by algorithm = 2,
Classified as Closed eyes.
(a) (b) (c)
fied as
Clas
he left most images show the normal state of eyes. For both
sub
9.Conclusion and Future Workhe paper attempts to determine eye state by first detecting
iris
References
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Fig. 10: (a) Detected Iris Radius by algorithm = 4 Classi
Open Eyes. (b) Detected Iris Radius by algorithm = 2
sified as Closed Eyes. (c) Iris pair not detected in section 6
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http://www.bioid.com/downloads/f
T
pair and then analyzing it. We achieve eye state identification
in five steps: (1) Face Detection (2) Eye candidates detection (3)
Tuning candidate points and (4) iris selection (5) Eye analysis.The contribution of this paper mainly starts from step 3, after eye
candidate points are found by AdaBoost. In step 3 candidate
points are shifted in such a way that greatly improves the
accuracy of iris localization and find radius values for candidate
points which also include irises of both eyes. Step 4 utilized three
metrics and can robustly filter out candidate points. For testing
purposes, three popular databases, Bern, Yale and BioID are
used. We will further work on the algorithm to make iris pair
localization more robust and add certain metrics to the eye
analysis step which can precisely determine the level of
drowsiness and eye state more automatically and robustly.
[12] http://cvc.yale.edu/projects/yalefaces/yalefaces.html
[13] http://iamwww.unibe.ch/~kiwww/staff/achermann.ht
T
http://www.bioid.com/downloads/facedb/index.phphttp://cvc.yale.edu/projects/yalefaces/yalefaces.htmlhttp://cvc.yale.edu/projects/yalefaces/yalefaces.htmlhttp://www.bioid.com/downloads/facedb/index.phpRecommended