nonintrusive iris image acquisition system based on a pan-tilt-zoom camera and light stripe...

8/12/2019 Nonintrusive Iris Image Acquisition System Based on a Pan-tilt-zoom Camera and Light Stripe Projection

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Nonintrusive iris image acquisition system basedon a pan-tilt-zoom camera and light stripeprojection

Soweon YoonHo Gi JungYonsei UniversitySchool of Electrical and Electronic Engineering134 Shinchon-dong, Seodaemun-guSeoul 120-749 Korea

Kang Ryoung Park Dongguk UniversityBiometrics Engineering Research CenterDepartment of Electronics Engineering26, Pil-dong 3-ga, Jung-guSeoul 100-715 Korea

Jaihie KimYonsei UniversitySchool of Electrical and Electronic Engineering134 Shinchon-dong, Seodaemun-guSeoul 120-749 KoreaE-mail: [email protected]

Abstract. Although iris recognition is one of the most accurate biometrictechnologies, it has not yet been widely used in practical applications.This is mainly due to user inconvenience during the image acquisitionphase. Specically, users try to adjust their eye position within smallcapture volume at a close distance from the system. To overcome theseproblems, we propose a novel iris image acquisition system that pro-vides users with unconstrained environments: a large operating range,enabling movement from standing posture, and capturing good-qualityiris images in an acceptable time. The proposed system has the follow-ing three contributions compared with previous works: 1 the capturevolume is signicantly increased by using a pan-tilt-zoom PTZ cameraguided by a light stripe projection, 2 the iris location in the large capturevolume is found fast due to 1-D vertical face searching from the usershorizontal position obtained by the light stripe projection, and 3 zoom-ing and focusing on the users irises at a distance are accurate and fastusing the estimated 3-D position of a face by the light stripe projection

and the PTZ camera. Experimental results show that the proposed sys-tem can capture good-quality iris images in 2.479 s on average at adistance of 1.5 to 3 m, while allowing a limited amount of movement bythe user. 2009 Society of Photo-Optical Instrumentation Engineers. DOI: 10.1117/1.3095905

Subject terms: iris image acquisition; pan tilt zoom camera; light stripe projection .

Paper 080693R received Sep. 3, 2008; revised manuscript received Jan. 6, 2009;accepted for publication Jan. 15, 2009; published online Mar. 10, 2009.

1 Introduction

Biometrics is a method for automatic individual iden tica-

tion using a physiological or behavioral characteristic.1

Thevalue of biometric systems can be measured with ve char-acteristics: robustness, distinctiveness, availability, accessi-bility, and acceptability. 1 Robustness refers to the fact thatindividual biometric features do not change over time andthey can be used repeatedly. Distinctiveness refers to thefact that each individual has different characteristics of thefeatures with great variation. Availability means the factthat all people ideally have certain biometric features inmultiples. Accessibility refers to how easy the acquisitionof biometric feature is, and acceptability refers to whetherpeople regard the capturing of their biometric features asnonintrusive.

In terms of the above characteristics, iris recognition is a

powerful biometric technology for user authentication be-cause it offers high levels of robustness, availability, anddistinctiveness. For robustness, it has b een proven that irisstructures remain unchanged with age. 2 A persons irisesgenerally mature during rst 2 years of age, and the nhealthy irises vary little for the rest of that persons life. 2

For availability, every person has an iris with complex pat-terns formed by multilayered structures. 2 Also, each indi-vidual has two distinguishable left and right iris patterns.

The distinctiveness of iris is shown by its unique and abun-dant phase structures. According to 2 million iriscomparisons, 3 binary code extracted from an iris imageshowed 244 independent degrees of freedom. This impliesthat the probability of two different irises agreeing bychance in mo re than 70% of their phase sequences is about1 in 7 billion. 4

The level of accessibility and acceptability of iris recog-nition, however, is lower than that of other biometric fea-tures such as the face, the ngerprint, or the gait recogni-tion. This is mainly due to the fact that it is difcult toacquire iris images. In terms of accessibility, iris imageacquisition is not simple; conventional iris recognition sys-tems usually require a well-trained operator, a cooperativeuser, adju sted equipment, and well-controlled lightingconditions. 5 Lack of any of these factors will lead to userinconvenience as well as poor quality iris capture. Accord-ing to a report 6 on participants experience of using variousbiometric authentication systems at an airport in 2005,common complaints about iris recognition systems wereabout positioning problems and the amount of time taken.

Conventional iris recognition systems such as IrisAc-cess3000 Ref. 7 and BMET300 Ref. 8 generally requirehigh user cooperation during iris image acquisition. Userstry to adjust their eye position to place their eyes in anacceptable position to provide the iris recognition systemwith an in-focus iris image. This positioning problemcomes from the fact that the capture volume of the conven-0091-3286/2009/$25.00 2009 SPIE

Optical Engineering 48 3 , 037202 March 2009

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tional systems is small. The capture volume refers to thevolume within which an eye must be placed for the systemto acquire useful iris images. 9 Once the iris of the user isplaced in the capture volume, users should stay in that po-sition without any motion until the system acquires a good-quality image. Since the capture volume is usually formedat a close distance from the camera, users face the systemclosely. The positioning takes a lot of time for users, and it

is likely to fail on untrained users who are relatively unfa-miliar with the system. Some children and disabled usersoften nd it difcult to follow the given instructions.

Toward convenient iris recognition systems for usersand civil applications such as immigration procedures atairports which target generally untrained users, two types of new iris image acquisition systems have been proposed.One is a portal system and the other is based on a pan-tilt-zoom PTZ camera. The portal system, which is calledIris-on-the- Move IOM suggested by Sarnoff Corporation, 9 enables the capture of iris images while userswalk through an open portal. IOM has a throughput up to20 persons / min when the users pass through the portalwith a normal walking pace of 1 m / s. However, a position

constraint remains because its capture volume is as small asconventional ones: 20 20 10 cm width heightdepth . Therefore, iris image acquisition fails if a users

irises do not pass through the small capture volume. Inaddition, the capture volume can not fully cover the heightvariations of users; children or very tall users may not bepermissible. They suggest a modular component to expandthe height of the capture volume; two cameras stacked ver-tically expand it by approximately 37 cm, and four camerasexpand it up to 70 cm. However, the stack of multiple high-resolution cameras would increase the costs proportional tothe number of cameras.

A PTZ camera can increase the capture volume greatly.Panning and zooming cover various position of users, andtilting covers height variation of the user. Early attemptsusing a PTZ function are reported by Oki IrisPass-M Ref.10 , Sensar R1 Ref. 11 , and Mitsubishi Corporation. 12

They are based on a wide-angle camera or a stereo visionsystem for locating the eye, and a narrow-angle camera forcapturing the iris image. For fast control of the PTZ cam-era, reconstructing the 3-D position of the iris is essential.First, 3-D coordinates can determine panning and tiltingangle as well as zoom factor. Second, depth informationbetween the iris and the camera from the 3-D coordinatesplays an important role to narrow the search range for op-timal focus lens position. The system from the MitsubishiCorporation uses a single wide-angle camera to detect aface, which leads to adaptive panning and tilting and esti-mates depth by disparity among facial features, which ob-viously takes a lot of time to get clear iris images. SensarR1 uses stereo matching for 3-D reconstruction. However,in stereo matching it is complicated and takes a long timeto detect the corresponding points between a pair of im-ages. The accuracy of the depth estimation can be degradedif users are far from the camera due to errors in the featurepoint extraction. To increase the accuracy of depth estima-tion of irises at a distance, the disparity of stereo camerasshould be large, and this will inc rease the system size.

Recently, Re tica Eagle-Eyes, 13 Sarnoff IOM Drive-Through system, 14 and AOptix system 15 have been intro-

duced as PTZ-based systems. Eagle-Eyes proposed the irisrecognition system with large capture volume 3 2

3 m and a long standoff 3 to 6 m . However, becauseof its system complexity, which consists of four camerasscene camera, face camera, and left and right iriscamerasthe cost and size of the system would be high. Inaddition, the capture time of the system is 6.1 s on averagefor a stationary subject, which is long compared to previoussystems, and users may feel an intrusiveness during imageacquisition. The organization and specication of other sys-tems are still unknown.

In this paper, we propose a novel iris image acquisitionsystem based on a PTZ camera guided by a light stripeprojection. A telephoto zoom lens with a pan-tilt unit ex-pands the capture volume greatly: 120 deg width 1 mheight 1.5 m depth . Thus, users do not need to make

an effort to adjust their position. Due to the PTZ ability, justone high-resolution camera is required to cover the wholecapture volume. For a fast PTZ control, which is necessaryto realize in a practical application scenario, we propose a3-D estimation method for the face based on a light stripe

projection. This contributes to fast face search and determi-nation of proper zoom and focus lens position. Since thelight stripe projection gives the horizontal position of a userin a real time, the pan angle is always determined immedi-ately and the users face can be found by searching a 1-Dvertical line rather than searching a 2-D area of the wholecapture volume. Once the face is detected, the depth be-tween the face and the PTZ camera is calculated with ahigh accuracy and it gives the initial zoom and focus lensposition based on relationships among distance, zoom lensposition, and focus lens position under xed magnication.We assumed minimally constrained user cooperation:standing naturally in the capture volume and staring at thePTZ camera for 1 to 2 s during autofocusing. Under this

assumption, we examined the feasibility of the proposedsystem in practical situations. The proposed system has thefollowing three contributions compared with previousworks: 1 the capture volume is greatly increased by usinga PTZ camera guided by a light stripe projection, 2 thePTZ camera can track a users face easily in the large cap-ture volume based on 1-D vertical face searching from theusers horizontal position obtained by the light stripe projection, and 3 zooming and focusing on the users irises ata distance are accurate and fast using the estimated 3-Dposition of a face by the light stripe projection and the PTZcamera. This paper realizes the PTZ-based iris image ac-quisition system, which is the most popular approach to thenext generation of iris recognition system and gives tech-

nical descriptions, so that it can be a helpful reference forresearchers in iris recognition eld.The rest of this paper is organized as follows. Section 2

describes the overall procedure of the proposed system andoutlines some design issues in terms of acceptability andaccessibility. Section 3 presents a method of 3-D face co-ordinate determination based on a light stripe projection.Section 4 describes zooming and focusing methods for thePTZ camera to get useful iris images based on the esti-mated depth in Sec. 3. Section 5 gives experimental resultson the feasibility of the proposed system, its availability for

Yoon et al.: Nonintrusive iris image acquisition system




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recognition of the iris images captured by the system, andthe accuracy and time required in practical application sce-nario. Finally, Sec. 6 provides conclusions.

2 System OverviewThe proposed system aims to acquire useful iris imagesunder an unconstrained user environment at a distance. Theunconstrained user environment means the following threefeatures. First, the large capture volume, as shown in Fig. 1,is created by a PTZ camera, which resolves positioningproblem. Second, both iris images of a user are obtainedeven when the user makes a small movement by a high-resolution image sensor incorporated in the PTZ camera.Third, processing time is made acceptable for users by us-ing the light stripe projection, which estimates the usersposition in real time. Figure 2 a shows the system congu-ration, which consists of a PTZ camera with a high-resolution image sensor, a wide-angle camera for detectinglight stripes, a light plane projector, and near-IR NIR il-luminators for imaging rich texture of irises.

To control the PTZ camera accurately and quickly tocapture a users iris images in the large capture volume, a3-D face coordinate estimation method based on the lightstripe projection can determine initial values for panning,tilting, zooming, and focusing. Thus, it helps narrow theranges for nding the optimal values of PTZ control. Figure2 b presents a ow chart for the iris image acquisitionprocedure of the proposed system. Light stripe projectiongives the horizontal position of a user in real time usinglight stripes on the users leg and the horizontal positiondirectly determines pan angle. Thus, the PTZ camera canturn toward the user and track the user when the user is inmotion. The users face is found on the 1-D vertical linenormal to the ground while the PTZ camera tilts upward.Once the face is detected, the distance between the PTZcamera and the face is calculated from the estimated 3-Dface coordinate. Using preestimated relationships amongdistance, zoom lens position, and focus lens position with a

xed magnication, the initial zoom and focus lens posi-tions are determined. Due to the high accuracy of the initialposition of each lens, only a small amount of focus rene-ment is required to get in-focus iris images. Since theheight of the user is xed after the 3-D face coordinate isdetermined, the face can be tracked using newly updatedhorizontal position and the height. Each part of the pro-posed system is designed to maximize user convenience, beeconomical, and work feasibly in practical applications.

2.1 PTZ Camera One part of our proposed system is the PTZ camera set,which consists of a pan-tilt unit, a telephoto zoom lens, anda high-resolution image sensor. Ranges for panning, tilting,and zooming should cover the entire target capture volume.Pan and tilt ranges of the pan-tilt unit are 360 and 90 deg,respectively, which are sufcient for our target capture vol-ume. Also, the speed of the pan-tilt unit is fast enough totrack a walking user; the pan and tilt speeds are 64 and43 deg / s, respectively.

The telephoto zoom lens should cover the depth range of the capture volume and have the desired standoff. The lensshould zoom in the irises of users who are in the target

Fig. 1 Large capture volume of the proposed system.

Fig. 2 System overview: a system conguration, and b owchart.





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depth range of the capture volume is 1.5 to 3 m so that theimages have the enough resolution for ir is recognition. Ac-cording to iris image quality standards, 16 the diameter of iris images must be greater than 150 pixels to be consideredas at least medium quality. Based on the fact that the diam-eter of the iris d iris is 1 cm and that of the image of the irisd imag e is 150 pixels, and the magnication M is 0.111 fromEq. 1 when a cell size of the image sensor is 7.4

7.4 m. Then, the required focal length can be estimatedusing Eq. 2 .

M =d

D=

d imaged iris

, 1

1 f

=1

D+

1d

, 2

where f represents the focal length, d represents the image-to-lens distance, and D represents the user-to-lens distance.In the proposed system, zoom lenses with focal lengthsvarying from 149.865 to 299.730 mm are generally re-

quired . The telephoto zoom lens used here has a focallength 17 of 70 to 300 mm, which guarantees that the reso-lution of the iris images is at least 150 pixels in diameter inthe target capture volume. In addition, the standoff is de-ned by the closest focusing distance of the zoom lens,which means that the lens can not focus on objects at thedistance closer than the closest focusing distance and is aphysical lens characteristic. According to the closest focus-ing distance of the lens, the standoff is 1.5 m.

A high-resolution image sensor of the PTZ camerashould capture useful iris images with enough resolution aswell as at a distance easily. Most iris image acquisitionsystems at a distance use a strategy of capturing a full-faceimage by a high-resolution camera instead of capturing justan iris image. One advantage of this strategy is that bothiris images can be obtained from a given high-resolutionface image, which shows better performance for iris recog-nition than one-iris matching. Another advantage is that atleast an iris remains in the captured image even when usersmove slightly. To get a full-face image guaranteeing thatthe diameter of each iris image is 150 pixels, the imageresolution on each side must be at least 1950 pixels if thewidth of a given face is around 15 cm and the diameter of the iris is around 1 cm. The resolutio n of the high-resolution camera in the proposed system is 18 4 megapixels2048 2048 pixels .

NIR illuminators radiating light in the 700- to 900-mmband are nec essary because even dark brown irises revealrich textures. 19 However, high-power illuminators are re-quired to obtain useful iris images for recognition at a dis-tance because the large f -number of the zoom lens reducesthe light energy incident to the image sensor. The f -numberrefers to the ratio of focal length to the effective aperturediameter. 20 In this case, the large f -number is caused by thelong focal length and small effective aperture of the zoomlens. The long focal length of the zoom lens is requiredwhen we zoom in on an object from a distance. The size of the effective aperture shrinks to hold the large depth of eld, which is necessary for robust focusing. In general, thepower of an NIR illuminator must be selected to maximize

the trade-off between obtaining sufciently bright imagesand guaranteeing eye safety. The overall intensity variationof captured images according to changing zoom factor iscompensated by adjusting camera gain and shutter speedbased on the distance between the camera and the user.

2.2 Light Stripe Projection Another part of the proposed system is the implementationof a light stripe projection. It consists of a light plane projector and a wide-angle camera. The projected light planeshould cover the horizontal range of the capture volume,which is 120 deg in width and 1.5 m in depth. The lightplane projector generates the NIR light plane with a wave-length of 808 nm, which is invisible to the human eye. Theangle of the light plane is 120 deg and is set up horizontallyat a height of around 20 cm to illuminate the given usersleg, as shown in Fig. 3. The intersection of the light planewith an object surface is visible as a light stripe in theimage. 21 The wide-angle camera detects the light stripes onthe users leg. The eld of view FOV of the wide-anglecamera is coincident to the angle of the light plane to ob-serve the whole light plane area. A visible cut lter is at-tached to the wide-angle camera to block visible light fromother light sources such as indoor illuminators and sunlight.

3 Estimation of 3-D Face CoordinatesEstimating the 3-D coordinates of a given users face con-sists of three phases: light stripe detection, horizontal posi-tion estimation, and vertical position estimation. Lightstripe projection provides the horizontal position, which de-termines the panning angle directly. It enables the PTZcamera to track the user horizontally until the user stops foriris recognition. Then, the face is found while the PTZ cam-era tilts along a 1-D line normal to the ground. Based onthe horizontal position of the user and the tilt angle where

Fig. 3 Detection of light stripes on the given users leg: a back-ground image with light stripes, b detected background light stripesin the ROI, c a new wide-angle camera image with the user, andd the light stripes on the users leg detected by CC-based back-

ground subtraction.





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the face appears in the center of the image, the 3-D coor-dinates of the face are determined in the PTZ camera coor-dinates.

3.1 Light Stripe Detection Light stripe projection is a 3-D reconstruction techniquethat is based on structured lighting. By projecting a lightplane into an object scene, the 3-D coordinates of image

points on the light stripes can be recovered from a singleimage. 21 In general, light stripe projection is implementedby the following three steps. The rst step is detecting en-tire light stripes in wide-angle camera images. These lightstripes include both those on the background objects andthose on a given users leg as shown in Figs. 3 a and 3 c .The second step is distinguishing the light stripes on theusers leg and transforming the center point of those lightstripes into an undistorted image coordinate. The third stepis reconstructing the 3-D coordinates of the center point inthe wide-angle camera coordinate system.

Light stripes in a wide-angle camera image are detectedby convolving each image column with the 1-D Laplacianof Gaussian LoG mask. This is based on the assumption

that light stripes appear at one point on each image columnbecause the light plane is scattered horizontally. A point of a column is regarded as light stripe if the point has themaximum LoG response in the column and the response ishigher than the given threshold. Figure 3 b presents thelight stripes detected from Fig. 3 a within the region of interest ROI , which corresponds to the horizontal regionof the capture volume.

Among the detected light stripes, those on the given us-ers leg are extracted by connected-component CC -basedbackground subtraction, which eliminates the light stripeson background objects. We assume that the backgroundlight stripe image is obtained in advance. If the light stripepoints in the adjacent columns are neighbors, they are re-garded as the CC. The CC-based background subtractionprocess removes the CCs in a new input image that overlappartially or totally with the background CC in the samelocation. Consequently, the light stripe remaining in the im-age is considered as the light stripe on a coming users leg.Figure 3 d shows the detected users light stripes from anew input image Fig. 3 c using CC-based backgroundsubtraction. The CC-based background subtraction is morerobust than pixel-based background subtraction, which canresult in strong errors even if the background or cameracongurations change slightly.

The center point of the light stripes on the users legs isused to estimate that users horizontal position. To compen-sate radial distortion on the wide-angle camera, the coordi-nates of the center point are rectied by the radial distortion

renement method addressed in Ref. 22. In this case, therectication process is done fast since the light stripes arerst detected in a raw image with radial distortion and onlya single pointthe center point of the light stripes on theusers legsis then transformed into an undistorted coor-dinate.

3.2 Horizontal Position Estimation The key idea of the light stripe projection technique for 3-Dreconstruction is to intersect the project ion ray of the ex-amined image point with the light plane. 21 In Fig. 4 a , the

reconstructed ray passing through both the image pointp x , y and the origin of the wide-angle camera coordinatesystem meets with the light plane at a certain pointP X wide , Y wide , Z wide . The 3-D coordinates of the intersec-tion are obtained 23 by Eq. 3 :

X wide = xb tan cos

f tan x sin + y cos

Y wide = yb tan cos

f tan x sin + y cos

Z wide = fb tan cos

f tan x sin + y cos

, 3

where represents the angle between the light plane andthe Y wide axis, represents the angle between the lightplane and the X wide axis, b represents the baseline, f repre-sents the focal length of the wide-angle camera, and x , yrepresents the rectied center point of the light stripe.

The 3-D coordinates corresponding to x , y are recon-structed directly if the focal length f of the camera and thegeometric parameters between the camera and the lightplane, , b, and are known. We assumed that =0 sincethe light plane is set up parallel to the ground. Then, Eq. 3is reduced to

Fig. 4 Reconstruction of the 3-D coordinates of the light stripe onthe use rs leg and its transformation to the PTZ camera coordinatesystem: 24 a general light stripe projection geometry, in this case,

=0; and b coordinate transformation from the wide-angle cameracoordinate system to the PTZ camera coordinate system.





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X wide = xb tan

f y tan

Y wide = yb tan

f y tan

Z wide = fb tan

f y tan

. 4

The remaining parameters, and b, are obtained by theleast-square estimation using the last equation of Eq. 4 bycollected data of the dis tance to an object Z wide and y coor-dinate of its light stripe. 24 Then, the reconstructed 3-D co-ordinates of a scene point on the light stripe,P X wide ,Y wide , Z wide , are obtained exactly from the imageof P , p x , y . This implies that 3-D reconstruction based onlight stripe projection is a real-time operation.

The reconstructed point P X wide ,Y wide , Z wide in the

wide-angle camera coordinate system is transformed intothe PTZ camera coordinate system. The PTZ camera coor-dinate system is the rigidly transformed wide-angle cameracoordinate system; it is rotated by around the X wide axisand then translated to d Z wide-PTZ in the direction of the Z PTZaxis, as shown in Fig. 4 b . Equation 5 shows the trans-formation from X wide , Y wide , Z wide to X PTZ , Y PTZ , Z PTZ ,where hPTZ represents the height of the PTZ camera fromthe ground:

X PTZY PTZ Z PTZ

=

1 0 00 cos sin 0 sin cos

X wideY wide Z wide

+

0hPTZ

d Z wide-PTZ

. 5

The height of the light plane Y PTZ is irrelevant in this case.As a result, X PTZ , 0 , Z PTZ represents the horizontal posi-tion of the user.

3.3 Vertical Position Estimation Figure 5 a illustrates the overall panning and tilting controlmethodology of the PTZ camera to nd the 3-D coordinatesof the users face. Based on the horizontal position of theuser X PTZ , 0 , Z PTZ , the panning angle pan is determineddirectly by

pan = tan1 X PTZ

Z PTZ. 6

Since the pan angle based on the horizontal position isgiven in real time, the PTZ camera is able to track the userhorizontally.

When the user stops, the face is found while the PTZcamera tilts. The tilting angle that locates the face in the

center of the image is found by using coarse and nesearching procedures. In the coarse searching phase, theface is detected in a few images obtained, while the PTZcamera tilts stepwise. Stepwise tilting partitions the heightof the capture volume exclusively, as shown in Fig. 5 b .The angle for a tilting step and the number of steps for thestepwise tilting are determined by the FOV of the PTZcamera and the height of the capture volume so that thePTZ camera captures a different view at each tilting angleas well as covers the entire range of height variations. Thisis more efcient than continuous tilting, which covers du-plicated views. If the face is detected at a certain stepwisetilting angle using the AdaBoost algorithm, 25 panning andtilting angles are rened to place the face in the imagecenter.

The ultimate tilt angle tilt determines the distance Dbetween the PTZ camera and the users face as follows:

D = Z d

cos tilt7

where Z d = X PTZ2 + Z PTZ

2 1/ 2.

4 Zoom and Focus ControlThe estimated distance between the PTZ camera and theusers face determines the initial zoom and focus lens po-sition so that it enables us to nd an optimal focus lensposition quickly. Finally, the focus renement process givesin-focus iris images.

4.1 Initial Zooming and Focusing Given a level of magnication, the desired zoom and focuslens position are determined if the distance between thecamera and the object is known. The magnication M ,which is xed for iris images to have enough resolution,yields the image-to-lens distance d based on the user-to-lens distance D in Eq. 1 . Since d is mapped 1-to-1 to thezoom lens position Zoom, D is eventually mapped 1-to-1 toZoom. Given D and Zoom values, the optimal focus lensposition Focus, which produces in-focus image is deter-mined.

To give the initial zoom and focus lens position at anarbitrary distance of D , the functional relationships, 1 be-tween D and Zoom and 2 between Zoom and Focus, areapproximated by collected observations. By changing thedistance D of a given user by 5 cm from the PTZ camerawithin the capture volume, the optimal zoom and focus lensposition that satisfy the conditions for iris images in termsof resolution and sharpness were recorded at each distance.The optimal zoom lens position at each distance was manu-ally adjusted so that the diameter of the iris image was150 pixels. The optimal focus lens position was searchedautomatically by assessing sharpness of the iris image se-quence continuously captured while the focus lens position

Fig. 5 Estimation of panning angle, tilting angle, and distance be-tween the PTZ camera and the face: a 3-D coordinate estimationof the users face and b 1-D face detection during stepwisetilting.24





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moved around the optimal focus lens position. The focuslens position in which the image had the highest focus mea-sure in the image sequence was chosen as the optimal focuslens position. The iris image assessment was based on thefocus measure kernel introduced in Ref. 26. The observa-tion of the optimal zoom lens position at each distance isshown in Fig. 6 a as a dotted line and that of the optimalfocus lens position at each zoom lens position is shown inFig. 6 b as a dotted line. A unit step of the zoom andfocus lens position refers to a step size of the steppingmotors, which rotate the zoom ring and the focus ring of the zoom lens. The amount of a step can be calculated bythe fact that rotating the zoom and focus lens fully requires30,000 and 47,000 steps, respectively.

Based on the preceding observations, the relationshipbetween D and Zoom is modeled as Eq. 8 . Zoom is in-

versely proportional to D . The unknown parameters p1 and p2 are estimated using singular value decomposition

SVD . Similarly, the relationship between Zoom and Fo-cus is modeled as Eq. 9 . Zoom and Focus have linearrelationship. The parameters q1 and q2 are found usingleast-squares estimation. The tting results are shown inFigs. 6 a and 6 b as solid lines, respectively.

Zoom = p1 p2 D

, 8

Focus = q1Zoom + q2 9

The D-based Zoom estimation proves to be more advan-tageous than the Zoom-based D estimation for focus rene-ment. That is, the former produces a narrower search rangefor the optimal focus lens position than the latter. It is ob-vious that the error in the estimated distance D is propa-gated to the error in the Focus determined by using thefunctional relationships. Clearly, minimizing the errorpropagation is necessary to conne the optimal focus lensposition in the narrow search range for fast focus rene-ment process. Less severe error propagation during D-based Zoom estimation can be explained by fundamentaltheorem, which means that the output of the probability

density function pdf , which passes through a function isinversely pr oportional to the magnitude of the derivative of the function. 27 Figure 7 compares two cases of error propa-gations. In D-based Zoom estimation, the uncertainty of theoutput Zoom is reduced, as shown in Fig. 7 a , since Zoomis inversely proportional to D . On the other hand, in Zoom-based D estimation, the uncertainty of D is increased, asshown in Fig. 7 b . As a result, an accurately estimated D ispreferred for fast focus renement.

4.2 Focus Renement The initial focus lens position estimated from D is usuallynot sufciently accurate because D contains errors fromhorizontal position estimation and tilting angle determina-tion. Focus renement is accomplished by searching for theoptimal focus lens position in the direction of maximizingthe focus measure of the captured iris images. Figure 8 ashows the focus measure of an iris image sequence cap-tured while the focus lens position moves around the initialfocus lens position. The ridge of the focus measure curve isregarded as the optimal focus lens position. The maximumvalue of the focus measure is 100. As shown in Fig. 8 b ,the iris image obtained at the initial focus lens positionshows high value in the focus measure and the initial focuslens position is near the optimal focus lens position.

For the focus measure of the iris images, the eye regionsare segmented from the full-face images. One simple eyedetection method is to nd the specular reections on theeyes generated by the NIR illuminators. Specular reec-tions usually appear as bright spots with high absolute gra-dient values and are surrounded with low gray values. Thecropped iris regions around the specular reec tions are con-volved with the 2-D focus assessment kernel. 26

The focus renement algorithm in Ref. 28 consists of two phases: the coarse and ne searching phases, as shownin Fig. 9. Let be a single step size of the focus lensposition for the ne searching phase. First, the coarsesearching phase roughly nds the optimal lens positionwith a large step size using the gradient-ascent method andnarrows the search range for the following ne searchingphase. In this stage, we set the step size of the coarse

Fig. 6 Calibrated initial zoom and focus lens positions. Functional relationships a between D andZoom and b between Zoom and Focus. Th e dotted lines indicate measured observations and thesolid lines indicate estimated relationships. 24





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searching as 4 . The focus lens moves by 4 synchronized

with the frame rate and the focus of the iris region in eachcaptured image is assessed. The direction in which the fo-cus lens moves in the next step is determined as the bestway to increase the focus measure. When the focus mea-sure reaches its ridge, the optimal focus lens position existsin the conned range of 4 . Second, in the ne searchingphase, the optimal focus lens position is found by movingthe focus lens precisely. The focus of the iris images isassessed while the focus lens position moves by in therange of the conned range in a direction opposite to that of the coarse searching phase. Therefore an in-focus imagewith a maximum focus measure is selected from the se-quence.

5 Experimental ResultsThe proposed iris image acquisition system was evaluatedbased on two characteristics: acceptability and accessibility.In terms of acceptability, the conditions for convenientenvironmentslarge capture volume, tolerance to natural

movements, and time required for iris image capturing

were veried by means of a feasibility test of the iris im-ages captured by the system and a time evaluation on vari-ous users who participated in using the system. In terms of accessibility, the accuracy of panning, tilting, zooming andfocusing control of the PTZ camera guided by light stripeprojection were analyzed.

5.1 Feasibility of the Proposed Unconstrained User Environments

The proposed system is designed to eliminate positioningproblems as well as to be tolerant of users natural move-ment while they are standing with natural posture. Theserequirements are achieved by providing the large capturevolume and by capturing face images at a high resolution,respectively. The capture volume was veried by a feasibil-ity test for the iris images acquired in the capture volumewhether they were available for iris recognition. The ro-bustness to user movements was analyzed by two factors:rst, the high-resolution camera was able to capture irises

Fig. 7 Error propagation of a D -based Zoom estimation and b Zoom-based D estimation. Initialamounts of error in a and b are the same length dotted box . Error propagation in a is less severethan that in b . The propagated amount of error in each case is illustrated as a stripped box. 24

Fig. 8 Focus measure of an iris image sequence obtained by changing the focus lens position and theinitial focus lens position estimated by the proposed method. a Focus measure of the entire imagesequence. The asterisk indicates the focus measure of the iris image at the initial focus lens position.b The enlarged dotted box in a Ref. 24 .





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under left-and-right movements, and second, the depth of eld of the PTZ camera was large enough to cover back-and-forth movements.

The feasibility of the captured iris images was examinedby calculating the Hamming distance with the enrolled irisimages of the same identity. The enrolled iris image refersto images acquired by a laboratory-developed iris acquisi-tion camera that captures focused iris images with morethan 200 pixels in diameter at the distance of 15 cm underthe NIR illuminators of 750 and 850 nm, which guaranteesgood-quality iris images for recognition. If the Hammingdistance between an enrolled image and an image capturedby the proposed system is lower than a given threshold, thecaptured iris image is identied as genuine. In other words,the image can be regarded as feasible for iris recognition.In the expe riment, the iris codes were extracted by the Ga-bor wavelet 26 and the well-known 19 threshold of Hammingdistance of the algorithm is 0.32.

For the feasibility test, the iris images of a user werecollected by moving the position of the user in the capturevolume. The depth from the PTZ camera to the userchanged by 5 cm within the range of 1.4 to 3 m, whichincluded the depth of the proposed capture volume i.e.,1.5 to 3 m . At each position, the zoom lens position wasdetermined to make the diameter of the iris images150 pixels. Then, the iris images were captured continu-ously while the focus lens position moved from 1000steps to +1000 steps around the optimal focus lens position.Figure 10 b shows several iris images that were capturedwhile the focus lens position changed when the user was ata distance of 2 m. The focus lens positions in this rangeproduced fully defocused iris images, in-focus iris images,and fully defocused iris images in turn. A sequence of irisimages captured at each distance was compared to the en-rolled iris images in terms of the Hamming distance. Figure10 a shows an example of the Hamming distance distribu-tion of the iris image sequence with respect to the focuslens position when the user was at 2 m. In this gure, wefound the available range of focus lens position that pro-

duced iris images with a lower Hamming distance than thethreshold. We called this range depth of focus.

5.1.1 Large capture volume Based on the Hamming distance evaluation results at eachdistance, we were able to verify the depth of the capturevolume and measure the depth of eld and the depth of focus of the system. The minimum and maximum focuslens positions of the available range at each distance aremarked in Fig. 11. The space between the minimum andmaximum focus lens positions represents the range inwhich the iris image has a Hamming distance lower thanthe threshold. In this gure, the depth of the proposed cap-

ture volume, 1.5 to 3 m, was veried as feasible; iris im-ages acquired in the capture volume were useful for recog-

Fig. 9 Focus renement algorithm. In the coarse renement phase,the ridge of the focus measure is found by moving the focus lens by4 . In the ne renement phase, the iris images are captured whilethe focus lens position moves by , and they are assessed in termsof focusing. The optimal focus lens position refers to the position atwhich the focus measure of the captured iris image is at the maxi-mum value.

Fig. 10 a Hamming distance distribution of an iris image se-quence of a user at 2 m with respect to focus lens position. T hedepth of focus at 2 m was obtained from this. b Some examples 29

of iris images captured at different focus lens positions at a distanceof 2 m.

Fig. 1 1 Depth of the capture volume, depth of eld, and depth offocus. 29





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nition because the camera was able to nd the optimalfocus lens position to acquire good-quality iris images interms of recognizability.

5.1.2 Tolerance of natural movements of users The depth of eld of the proposed system was able to copewith back-and-forth user movements when the user wasstanding with natural posture. The depth of eld refers tothe permis sible depth variations of user under xed lensconditions. 29 This means that the iris images of a user cap-tured while the user moves within the depth of eld are stillavailable for recognition without additional focusing con-trols. The depth of eld at each distance can be estimated inFig. 11. Figure 12 a shows the estimated depth of eldwith respect to distance. Note that the graph in Fig. 12 alooks continuous because the curve-tting results of twolines in Fig. 11 were used for the evaluation of the depth of eld. The depth of eld tended to increase when the dis-tance between the camera and the user increased. In thecapture volume, the depth of eld was 5 to 9.5 cm, whichcovered the inevitable movements of users during the irisimage acquisition phase.

While the depth of eld shows system tolerance to back-and-forth user movements, the strategy of capturing full-face images with the high-resolution camera instead of cap-turing only iris images achieves tolerance to left-and-rightmovements. In normal situations, both iris images arecropped from full face images. Even if the users positionshifts during the process, at least one iris usually still existsin the image. However, if a fully zoomed iris image iscaptured in 640 480 pixels with a standard camera, it re-quires precise panning and tilting to capture the eye re-gions. Unfortunately, in general, this means that the iris canbe lost from the image even if the user moves slightly.

We compared the motion tolerance of capturing a fullface with that of capturing an eye when the system wasexposed to the natural user movements. If the user appearedin the capture volume, the proposed system captured boththe iris images from a high-resolution full-face image.Then, the user kept the initial position and stood with natu-ral posture for a minute. At the same time, the PTZ cameracaptured the face images every second without any pan-ning, tilting, zooming, or focusing. This experiment wasperformed on 11 people and 10 times each. Figure 13 a

shows the initial full-face image captured by the high-resolution PTZ camera and the dotted box indicates the640- 480-pixel region around the iris. When the usermoved, the high-resolution camera still contained bothirises while the 640- 480-pixel region sometimes lost theiris, as shown in Fig. 13 b . The movement of the users wasmeasured by the pixel distance between the iris center of the initial frame and that of the following frame, shown asd in Fig. 13 b . Figure 13 c shows a histogram of d . Themean and standard deviations of d were 122.86 and

Fig. 12 a Depth of eld and b the depth of focus of the proposed system with respect to distance. 29

Fig. 13 User movements measured on image when users werestanding naturally: a initial frame acquired by the PTZ camera athigh resolution, where the box indicates a region of 640

480 pixels; b an image from a 1-min image sequence in whichthe eye escaped the initial eye region, here d is the distance be-tween the iris center of the initial frame and that of the current frame;and c the histogram of d .





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93.21 pixels, respectively. Considering that the margin was320 pixels in width from the center of the initial 640-

480-pixel region, average movements of users causedpartial occlusion of eye regions, which led to a failedboundary detection of the eye and iris regions. Movementsover 200 pixels occurred about 17% of the time, whichmeant that the iris pattern was lost from the FOV. However,the full-face images always contained eye regions during

the movements.5.1.3 Accessibility for in-focus iris images The depth of focus refers to the permissible error range of the focus lens position to obtain feasible iris images forrecognition. In the experiments, the depth of focus wasevaluated in the sense of iris recognition rather than in thesense of optics, since even slightly defocused iris imagescould be identied correctly. The depth of focus is a mea-sure of the characteristic of accessibility since systems withlarge depth of focus do not require elaborate focusing algo-rithms. Furthermore, large depth of focus brings fast irisimage acquisition because the optimal focus lens position isfound using large step sizes during the ne searching pro-

cess. As shown in Fig. 12 b , the depth of focus of theproposed system showed variations within 500 to 2000steps in the capture volume. This means that the controlerror of focusing was acceptable by at least 500 steps.Since the error of the initial focus lens position was around1000 steps see the next section , either the iris imagescaptured at the initial focus lens position were available if the initial focus lens position was in the depth of focus, orthe optimal focus lens position could be found within theconned search range even if the initial focus lens positionwas out of the depth of focus.

5.2 Accuracy of PTZ Control Based on Light Stripe Projection

In the proposed PTZ control method, an accurate distanceestimation between the PTZ camera and the given usersface is necessary to determine the initial zoom and focuslens positions, which can narrow the search range for opti-mal focus lens position. However, direct accuracy evalua-tion of the estimated distance is difcult because it is noteasy to obtain the precise ground truth data of the distancebetween two points in a 3-D space. Instead of measuringthe distance estimation accuracy directly, the accuracy of the initial focus lens position was measured by observingthe error between the optimal focus lens position and initialfocus lens position. If the initial focus lens position is nearthe optimal one, PTZ control can be regarded as accurate.

5.2.1 Error of horizontal position estimation of the

user The initial focus lens position error was induced by thehorizontal position estimation error and the face detectionerror. The error of horizontal position estimation using lightstripe projection was due to limited image resolution; thatis, quantization error. As shown in Fig. 14 a , a single pixelin the image plane is matched with not a single point inthree dimensions, but a certain area. Therefore, estimateddepth by light stripe projection has uncertainty. Anotherfeature is that image of the light stripe on a close objectshows a lower level of ambiguity in depth than that of the

light stripe on objects further away. The dotted line in Fig.14 b represents the range of quantization error of depthestimation, which has more ambiguity in depth estimationat a further distance.

To evaluate depth estimation accuracy, we compared thehorizontal distance estimated by light stripe projection withthe distance measured by a laser distance-meter. A planeboard was used for accurate experiment, and its light stripeimages were collected by changing the distance from thewide-angle camera to the plane in the capture volume. InFig. 14 b , measured errors are shown as a solid line. In thecapture volume, depth estimation by light stripe projectionwas mostly successful within the quantization error boundand the error bounded within 2 cm. However, depth esti-mation errors on objects at around 1.5 m occurred beyondthe quantization error bounds. This arose from the detectionerror of the center point of a light stripe because the lightstripes at a close distance were relatively thick, and then thecenter point changed with variations of 1 pixel at a time.

Also, the error curve formed a zigzag shape since the planewas located inside the scene depth coverage of a pixel atrandom.

5.2.2 Face detection error Face detection errors also affected the accuracy of the ini-tial focus lens position. W e used a well-known face detec-tor provided by OpenCV. 30 Since the face detector wastrained under visible illumination, we needed to show theperformance consistency of the face detector under NIRillumination. We collected 540 images from 35 users that

Fig. 14 Quantization error of depth estimation in light stripe projec-tion: a uncertainty of depth estimation due to limited pixel reso-lution and b measured errors at 43 different distances solid lineand quantization error bound according to the distance dotted line .





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Figures 17 b 17 e present iris images that were cap-tured by the proposed system at a distance. Compared toenrolled iris images acquired by laboratory-developed irisacquisition camera Fig. 17 a , they were of high quality interms of recognizability; the Hamming distances betweenthe enrolled image and the captured images were Fig.17 b , 0.201; Fig. 17 c , 0.189; Fig. 17 d , 0.240; and Fig.17 e 0.240, which were lower than the given threshold,0.32. Figure 18 presents several examples of both iris im-ages of various users captured by the proposed system at adistance during a real demonstration.

5.3 Time Required for Iris Image Acquisition In this section, we present the time of the entire acquisitionprocess. We measured the time required by nine partici-pants in a real situation, which included one experienceduser, two relatively less experienced users, and six inexpe-rienced users. The participants were instructed to stand atany position within the capture volume and stare the PTZcamera during the image acquisition process. The time fortilting, initial zooming and focusing, and focus renementwas measured separately right after the user stopped. Thetime for panning was not recorded, because panning wasdone continuously while the user moved. Table 1 shows theaverage time evaluation for each phase. The frame rate of the PTZ camera was 8 frames / s. The average time requiredto obtain in-focus iris images using the proposed systemwas 2.479 s with Intel Core2 CPU, 2.4 GHz , which iscomparable to conventional iris recognition systems.

The time for tilting depends on the users height. Theprocess includes time for tilting the PTZ camera and detect-ing the face. However, since only a few of the images cap-tured during stepwise tilting were used, the time variationsdue to height variation were not critical. The time requiredfor initial zooming and focusing was fairly constant be-cause the lens positions were directly determined by the3-D face coordinates. There were slight variations accord-ing to distance; the zoom lens rotated more and the focuslens rotated less when a user was farther away from thePTZ camera. But the time variations were not signicant.For the time required for focus renement, the proximity of the initial focus lens position to the optimal focus lens po-sition was a critical factor. Based on the nding that theinitial focus lens position is usually located around the op-timal focus lens position within fewer than 1000 steps, weset a single step size for the ne searching phase as 50steps, and a consequent step size for the coarse searchingphase 4 as 200 steps. This means that the coarse searchingphase took less than ve frames in most cases. Moreover,the ne searching phase used a rmly bounded number of frames within a conned range. The experimental results

show that eight to nine frames were taken to obtain in-focusiris images during the focus renement stage.

6 ConclusionsA novel iris image capturing system was proposed to im-prove acceptability and accessibility of iris recognition sys-

Fig. 17 Iris images captured at a distance: a enrolled image cap-tured by a conventional iris recognition system, and iris images ofthe same person captured using the proposed system at b 1.5, c

2.0, d 2.5, and e 3.0 m Ref. 29 .

Fig. 18 Examples of left and right iris images captured by the pro-posed system. The distances are the estimated value by light stripeprojection and tilt angle estimation and show the quality of both irisimages captured at various distances.





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tems. Acceptability is achieved in terms of user position,movement, and time required. A large capture volume of 120 deg width 1 m height 1.5 m depth enables us-ers to pay less attention to positioning at a distance andmakes the proposed system applicable to users of variousheights. A high-resolution PTZ camera and a sufcientdepth of eld result in the advantage that users can standnaturally while the iris images are captured. Both iris im-ages are successfully cropped from full-face images cap-tured by the high-resolution camera when the user movesslightly to the left and right. The depth of eld of the pro-posed system shows that it is tolerant to back-and-forthmovements. It takes an average of 2.5 s to capture the in-

focus iris images.Accessibility is achieved by estimating the face coordi-nates based on real-time detection of the users horizontalposition using light stripe projection and by holding enoughdepth of focus. The horizontal position of the user deter-mines the pan angle exactly and helps the face be detectedin a 1-D vertical line. Then the estimated distance betweenthe PTZ camera and the face determines the initial zoomand focus lens positions with high accuracy. Since the facecoordinate information reduces most parts of the PTZ con-trol from searching and optimization problems to determin-istic ones, PTZ control is performed quickly. In addition,the accuracy of the initial focus lens position contributes tofast focus renement and a sufcient depth of focus elimi-nates a need for an elaborate focus renement algorithm.Proposed system has the following three contributionscompared with previous works: 1 the capture volume islargely increased by using a PTZ camera guided by a lightstripe projection, 2 the PTZ camera can track a users faceeasily in the large capture volume based on 1-D verticalface searching from the users horizontal position obtainedby the light stripe projection, and 3 zooming and focusingon the users irises at a distance are accurate and fast usingthe estimated 3-D position of a face by the light stripeprojection and the PTZ camera.

For further research, efcient illumination control is re-quired. Because the combination of a high-resolution cam-era and a lens with a large f -number reduced the total in-cident energy of light, we used bulky illuminators, whichemitted NIR light continuously. Instead, low-power syn-chronized ash illuminators can be one of the solutions. Inthe future, the proposed system will be applied to movingusers by solving the degradation of iris image quality thatcan occur with motion blurring.

Acknowledgments This work was supported by the Korea Science and Engi-neering Foundation KOSEF through the Biometrics Engi-neering Research Center BERC at Yonsei University.

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on Dec. 31, 2008 .7. IrisAccess 3000, LG Online available at http://www.lgiris.com/ ps/products/previousmodels.htm accessed on Dec. 31, 2008 .

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24. S. Yoon, H. G. Jung, J. K. Suhr, and J. Kim, Non-intrusive irisimage capturing system using light stripe projection and pan-tilt-zoom camera, in Proc. IEEE Computer Society Workshop on Bio-metrics in association with CVPR07 , pp. 17 June 18, 2007 .

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Table 1 Time required for each stage and average time to obtain feasible iris images unit: seconds .

TiltInitial Zoomingand Focusing

FocusRenement Total

Average time 0.857 0.438 1.183 2.479



http://dx.doi.org/10.1109/5.628669http://dx.doi.org/10.1109/5.628669http://dx.doi.org/10.1023/A:1012365806338http://dx.doi.org/10.1023/A:1012365806338http://dx.doi.org/10.1109/JPROC.2006.884092http://dx.doi.org/10.1109/JPROC.2006.884092http://dx.doi.org/10.1023/B:VISI.0000013087.49260.fbhttp://dx.doi.org/10.1023/B:VISI.0000013087.49260.fbhttp://dx.doi.org/10.1109/JPROC.2006.884092http://dx.doi.org/10.1023/A:1012365806338http://dx.doi.org/10.1109/5.628669


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Comput. Vis. 57 2 , 137154 2004 .26. J. Daugman, How iris recognition works, IEEE Trans. Circuits

Syst. Video Technol. 14 1 , 2130 2004 .27. A. Papoulis and S. U. Pillai, Probability, Random Variables, and

Stochastic Processes , 4th ed., McGraw-Hill, New York 2002 .28. M. Subbarao and J. Tyan, Selecting t he optimal focus measure for

autofocusing and depth-from-focus, IEEE Trans. Pattern Anal. Mach. Intell. 20 8 , 864870 1998 .

29. S. Yoon, K. Bae, K. R. Park, and J. Kim, Pan-tilt-zoom based irisimage capturing system for unconstrained user environments at adistance, in Proc. 2nd int. Conf. on Biometrics, Lecture Notes in

Computer Science , Vol. 4642 , pp. 653663, Springer, Berlin 2007 .30. OpenCV Online , available at http://sourceforge.net/projects/ opencvlibrary/ accessed on Dec. 31, 2008 .

Soweon Yoon received her BS and MS de-grees from the School of Electrical andElectronic Engineering, Yonsei University,Seoul, Korea, in 2006 and 2008, respec-tively. She is currently a PhD student withthe Department of Computer Science andEngineering, Michigan State University. Herresearch interests include pattern recogni-tion, image processing, and computer visionfor biometrics.

Ho Gi Jung received his BS, MS, and PhDdegrees in electronic engineering from theYonsei University, Seoul, Korea, in 1995,1997, and 2008, respectively. He has beenwith the Mando Corporation Global R&DH.Q. since 1997. He developed environ-ment recognition algorithms for a lane de-parture warning system LDWS and adap-tive cruise control ACC from 1997 to 2000.He developed an electronic control unitECU and embedded software for a elec-

trohydraulic braking EHB system from 2000 to 2004. Since 2004,he has developed environment recognition algorithms for an intelli-gent parking assist system IPAS , collision warning and avoidance,and an active pedestrian protection system APPS . His interestsare automotive vision, embedded software development, driver as-sistant systems DASs , and active safety vehicles ASVs .

Kang Ryoung Park received his BS andMS degrees in electronic engineering fromYonsei University, Seoul, Korea, in 1994and 1996, respectively, and his PhD degreein computer vision from the Department ofElectrical and Computer Engineering, Yon-sei University, in 2000. He was an assistantprofessor with the Division of Digital MediaTechnology, Sangmyung University, from

March 2003 to February 2008 and sinceMarch 2008 he has been an assistant pro-fessor with the Department of Electronics Engineering, DonggukUniversity. He also has been a research member of BERC Biomet-rics Engineering Research Center . His research interests includethe computer vision, image processing, and biometrics.

Jaihie Kim received his BS degree in elec-tronic engineering from Yonsei University,Seoul, Korea, in 1979 and his MS degree indata structures and his PhD degree in arti-cial intelligence from Case Western Re-serve University, Cleveland, Ohio, in 1982and 1984, respectively. Since 1984, he hasbeen a professor with the School of Electri-

cal and Electronic Engineering, Yonsei Uni-versity. He currently directs the BiometricEngineering Research Center in Korea. His

research areas include biometrics, computer vision, and pattern rec-ognition. Prof. Kim currently chairs the Korean Biometric Associa-tion.


http://dx.doi.org/10.1023/B:VISI.0000013087.49260.fbhttp://dx.doi.org/10.1109/TCSVT.2003.818350http://dx.doi.org/10.1109/TCSVT.2003.818350http://dx.doi.org/10.1109/TCSVT.2003.818350http://dx.doi.org/10.1109/34.709612http://dx.doi.org/10.1109/34.709612http://dx.doi.org/10.1109/34.709612http://dx.doi.org/10.1109/34.709612http://dx.doi.org/10.1109/34.709612http://dx.doi.org/10.1109/34.709612http://dx.doi.org/10.1109/TCSVT.2003.818350http://dx.doi.org/10.1109/TCSVT.2003.818350http://dx.doi.org/10.1023/B:VISI.0000013087.49260.fb

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