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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

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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.php