A Standoff System for Noncooperative Ocular Biometrics

A Standoff System for Noncooperative Ocular Biometrics Plamen Doynov and Reza Derakhshani, Ph.D. Department of Computer Science Electrical Engineering University of Missouri at Kansas City Kansas City, MO 64110, USA that affect image quality and overall system performance. Section four describes the development of a standoff ocular biometric system using system integration of commercially available, off-the-shelf components. In section five, we report the corresponding results for ocular images of 28 volunteers at distances of up to 9 meters. In conclusion, we outline the key attributes of the imaging system for standoff ocular biometrics, the challenges we faced, and the future work based on the lessons learned. Abstract—The iris and more recently vascular patterns seen on the white of the eye have been considered for ocular biometrics. The non-contact nature, uniqueness, and permanence of ocular features makes them promising. Among new challenges are to develop commercial systems for less constrained environments and at extended distances. Such systems need to have minimal burden on the user and be robust for non-cooperative users. We present the design and development of standoff system for noncooperative ocular biometrics using system integration approach. Review of existing commercial and experimental long-range biometric systems is presented. The process of selection of sensors and illumination techniques is described. The development of user interfaces and algorithms for a working prototype is explained. The performance is evaluated with images of 28 subjects, acquired at distances up to 9 meters. The conflicting requirements for the design of this standoff biometric system, and the resulting performance limitations with impact on image quality are discussed. II. A. Standoff systems for Ocular Image Acquisition At long distances, capturing the eye with sufficient resolution and quality is a challenging proposition [15-17]. The challenges are elevated even further when the imaging system has to work well without cooperation from the user. Proenca et al. address important issues and trends in noncooperative iris recognition, and have created UBIRISv2 database of iris images captured “on the move” and “at a distance” [18-20]. The authors use visible spectrum for imaging as an alternative to the customary near infrared (NIR). Wheeler et al. [21] describe a stand-off iris capturing system designed to work at up to 1.5 m using a pair of cameras with wide field of view for face localization and an iris camera to capture the iris. Dong et al. [22] discuss the design of a system to image the iris at a distance of 3 meters. The “Iris on the Move” system of Sarnoff Corporation has also a reported standoff distance of 3 meters. It is a portal-style system with a 210 mm, F/5.6 lenses [23]. Du et al. [24] describe and use the IUPUI multi-wavelength database, acquired at 10.3 feet from the camera to the subject using a MicroVista NIR camera with Fujinon zoom lens. AOptix Technologies [25] uses adaptive optics with wavefront sensing and close loop control for a standoff system with a work volume at 2 to 3 meters from the camera. Retica reports that their multi-biometric system achieves 77% true match rates at 4.5 meters on first attempt and 92% after three attempts [26]. Keywords-Ocular biometrics; Standoff biometric system; Noncooperative biometrics. I. INTRODUCTION Biometric technologies have been implemented in many application areas and are replacing traditional authentication methods [1-3]. Ocular biometrics refers to the imaging and use of characteristic features of the eyes for personal recognition. The proliferation of ocular biometrics is based on its inherent advantages [4, 5] and it is made possible by recent progress in related technologies and processing algorithms [6-8]. Traditional face and fingerprint recognition may also be augmented by additional biometric traits, such as ocular, for more accuracy and security [9]. However, many challenges remain, especially for iris image acquisition in unconstrained conditions and without the necessary degree of user cooperation [10-12]. Research teams and commercial developers have responded by creating uni- and multi-modal systems for real-world conditions [13, 14]. The goal is robust performance in variable lighting conditions and subject-tocamera distances for moving subjects, off-angle images, and other factors that generally diminish captured image quality. During next section of this paper, we review some notable long-range ocular biometric systems and describe their important parameters and limiting factors. Section three outlines the requirements of the front-end imaging system of a standoff ocular biometric system. We describe key components 978-1-4673-2709-1/12/$31.00 ©2012 IEEE ACQUISITION OF OCULAR BIOMETRIC TRAITS B. Augmented Standoff Acquisition Systems Most current ocular biometric systems are based on the unique iris patterns of the human eye. Their performance depends directly on the iris image quality, which is adversely affected by distance. Recently, improvements using additional ocular modalities have been investigated [9, 12, and 34]. Simultaneous acquisition of iris, vascular patterns on the white of the eye, and periocular patterns may also reduce user constrains or requirements for compulsory user cooperation. 144 average iris size of 10mm. Because of this, one customary approach is to use a wide field-of-view camera to locate the eyes in tandem with a second, narrow field-of-view camera for imaging. Localization of the eyes for subjects on the move is not a trivial task either. Again, many systems use a camera with a wide field of view to locate the face and subsequently the eyes, and a high resolution, high-magnification camera for iris capture. To cover a wider field of view, the high resolution camera maybe mounted on a pan and tilt stand, and use a lens with optical zoom (PTZ). In this case, the mechanical stability, speed, and pointing accuracy of the PTZ system become crucial. Even at only 1 meter standoff, 100 microns resolution for the iris capture requires 100 micro-radians (0.006 degrees) pointing stability [23]. Extreme standoff distances are limited by the governing laws of light propagation and the capability and price of current technology and components. In next section, we describe the design and construction of a standoff imaging system using system integration of commercially available components with low to medium cost. Periocular features maybe useful for long distance recognition, however they are not as specific as those of iris or vasculature seen on the white of the eye. The latter is especially amenable to being captured at longer distances in visible spectrum and with off-angle iris. In an effort to extend the depth of field, another challenge in standoff ocular biometrics, Boddeti and Kumar [27] investigate the use of wavefront-coded imagery for iris recognition. They conclude that wavefront coding could help increase the depth of field of an iris recognition system by a factor of four. McCloskey et al. [28] explore a “flutter shutter” technique to acquire focused iris images from moving subjects eliminating motion blur. Researchers have explored “structured” light, visible spectrum, and imaging under different wavelength illumination as opposed to the NIR range (700 to 900 nm), which is typically used in commercial systems. Ross et al. [29] investigate imaging with illumination in the 950nm to 1650nm range at short distances. Grabowski et al. [30] describe iris imaging that allows characterization of structures in the iris tissue. He et al. [31] design their own camera for iris capture using a CCD sensor with 0.48 M pixels resolution and a custom lens with 250mm fixed focus. They use LED light source at 800nm and NIR band-pass filter to minimize specular reflections from the cornea of the eye. III. IV. AN ACQUISITION PLATFORM FOR NONCOOPERATIVE, LONG-RANGE OCULAR BIOMETRICS The following describes our novel acquisition platform for long-range ocular biometrics to image iris in NIR from standoff distances up to 10 meters and possibly without necessary cooperation from the subjects. PARAMETERS AND REQUIREMENTS OF STANDOFF OCULAR BIOMETRIC SYSTEMS The acquisition of quality images is the most important step in standoff ocular biometrics. There are specific requirements and performance challenges to the image capturing equipment. Proximity to the subject, illumination, and viewing angle are among confounding variables. Moving subjects have a limited residence time in the field of view and within the imaging depth of field. Even with some degree of cooperation, the orientation of face and eyes is not always perfect. Image resolution decreases with increased distance. The collected light by the lens aperture decreases in inverse proportion to the square of the distance. Imaging with higher f-number increases the depth of field but limits the amount of collected photons and consequently, requires longer exposure times (or increased illumination intensity). Imaging with long exposure time is prone to motion blur. In their comprehensive tutorial [32], Matey and Kennell examine the requirements for iris capturing at distances greater than one meter. The authors describe many relevant parameters, including wavelength, type of light source and eye safety, required characteristics of the lens and signal to noise ratio, capture volume and subject’s residence time. Describing the “Eye on the move” system, Matey summarizes the requirements for a standoff iris imaging system [33]. He indicates the need for approximately 100 microns resolution at the eye (100 pixel across the iris); 40 dB signal to noise ratio (SNR); 90 and 50 levels of intensity separation between irissclera and iris-pupil boundaries, respectively, for an 8-bit imaging sensor. To cover a double door passing area, Matey calculates that a system needs 150 mega-pixels sensor(s) to achieve a 100-pixel resolution across the imaged iris, given an A. Design Approach and Considerations The original idea was to focus on development of the front end optics and image sensor for long range acquisitions. Furthermore, to relax the requirements for subject cooperation, we investigated electronic and electromechanical components required to locate a subject’s eyes, detect optimal gaze, and acquire an image (or images) with sufficient quality. Our design concept addressed the following three functional aspects: 1. Eyes and iris localization with gaze detection and alignment: using a PTZ mechanism, camera is to make eye contact with the subjects (rather than the opposite, which requires cooperative subjects): • Customized Eyebox2 (XUUK™ Inc., Kingston, ON, Canada) system for long-range person/face/eyes and gaze detection; • Ability to control simultaneously multiple iris cameras for crowd scanning. 2. Use advance imaging techniques, especially lucky imaging, for long range acquisition of the iris: • High-magnification precision optics; • NIR-enhanced high-speed image sensor in burst mode (high frame rate); • Real-time algorithms to evaluate image quality and to select the best image from a sequence; • Synchronized active illumination, and 3. Subject/eye tracking until a good quality image is obtained. 145 has advantages such as a short minimum m focusing distance (5m), ease of coupling with variety y of digital cameras, and a flexible camera adapter with directt access for imaging target location and focus adjustment. In th he final design, we selected an eyepiece with a fixed power which makes placement of the image sensor behind the eyepiecce less critical and, more importantly, it produces less vignettting (darkening around the edges of an image) compared to a ty ypical zoom eyepiece. Because of the need for high magnification m and to reduce the effect of subject-camera moveements, a standoff system requires short exposure times using g “fast” lenses and sensitive image sensors with larger pix xels, plus proper active illumination. Historically, NIR illumination is used to facilitate the imaging of irises wiith dark pigmentation. For multimodal ocular biometrics, visiible spectrum can be used for periocular imaging and to imag ge the vasculature seen on white of the eye (wavelengths in the t green bandwidth). NIR illumination, however, does not evoke e the protective pupil constriction reflex and thus the usee of extreme light intensity for standoff system is limited. IR-A A (near IR, 780 to 1400nm) reaches the sensitive cells of the retina, and thus for high irradiance sources the retina is at risk from acute exposures from localized light sources. IR-B (mid IR, 1400 to 3000nm) and IR-C (far IR, 3000nm to 1mm m) present risk to both the skin and the cornea from “flash burrns.” The heat deposited in the cornea may be conducted to the eye’s lens and cause clouding (albeit with very long exposure times). The IR illumination intensity for short expo osures should be limited to The performance requirements for resoluttion are based on the SNR and optical resolution of the acquuired signal. The quality of images at a distance is related to thhe lens’ aperture, residence time (frame exposure duration) and light intensity at the imaging sensor. Practical usability and thhe need to avoid blurring because of subject movements are additional constrains. Maximum illumination is limited by eye safety factors. Thus, compliance with ANSI/IESNA Standard RP27.1-96 and its testing methodology is criticaal. In late 2008, a newer standard, referred to as IEC 62471-20006, was adopted. It addresses the photo-biological safety oof lamps, lamp systems, and specifically the safety of L LED sources, as applicable to ocular imaging illumination. Design considerations have to include thee diffraction limit of a lens and its dependence on the wavvelength and the aperture diameter (optical resolution covvariates). These requirements on optical system and image sensor are more stringent in case of non-cooperative subjects. B. Component Selection and Design Implemeentation The focus areas, while implementing the standoff system design, were: a) optics; b) illumination; c) im maging sensor(s); d) eye/gaze detection and tracking; and e) near real-time ocular image quality metrics. Optical front end is the most important paart of the imaging system with three main components: long focaal length lens; an eyepiece for additional magnification; and aan image sensor (camera) attached to the optics. After conssidering different telephoto lenses, we first used Infinity’s K2/S remote microscope (Infinity, Boulder, CO, USA) coupled with a digital camera (Fig. 1.a). K2/S is an excellennt optical system with many advanced features. However, with its small aperture and limited field of view, K2/S requires intense illumination and pointing accuracy, even att short distances (e.g. less than 3m). To obtain the necessary reesolution at up to, we resorted to digiscoping as one of the sim mplest, yet most effective methods for high magnification imagging. Just as with camera lenses, a high performance glass objecctive is needed to produce the sharpest images with the best coolor reproduction and optical resolution. For cost considerations, first we explored the performance of Meade’s LX990-ACF (Meade Instruments, Irvine, CA, USA) Schmidt-Casssegrain telescope (Fig. 1.b), because of its large aperture and tthe intrinsic high photon collection efficiency of a reflective teleescopes. Εγ Δγ < 1.8 t3/4 (1) where Eγ is the spectral irradiance in W/cm2, Δγ is the spectral bandwidth in nm, and t is the exposure e time in seconds (International Commission on Non-ionizing Radiation Protection, ICNIRP 1996, 2000). We W used a digital hand-held multispectral intensity meter, PM M100 with S120B sensor, (ThorLabs, Newton, NJ, USA) to measure and monitor illumination intensity levels, and also to evaluate the light capturing capability of STS80 HD. mercial light sources, we After testing different comm selected LIR850-25 (LDP LLC – MaxMax.com, M Carlstadt, NJ, USA) with an effective range of up u to 150 m (Fig 2). Each source has 147 IR LED and can opeerate in a synchronous flash mode with the camera. Figure 1. Telescopes coupled with cameras for standooff ocular imaging testing: a) Infinity K2/S; b) Meade LX90; and c) Swarrovski STS80 HD. In parallel, we evaluated the high-definiition STS80 HD spotting scope (Swarovski Optik North Am merica, RI, USA) with an 80mm objective lens and availabble selection of magnifying and optical-zoom eyepieces (Fig. 1.c). The objective lens focal length is 460mm and the weight is 1330g. Besides typical limitations of refractive telescopes, STS80 HD Figure 2. The system for standoff ocular im maging with Swarovski STS80 HD telescope and two LIR850--25 lifgt sources. 146 one person detected and “looking” (gaze ( directed axially at the XUUK camera). Fig. 5(d) demonsttrates detection of a person looking at the camera from about a distance of 7.5 meters. We tested several cameras with enhancedd NIR sensitivity. The SMX-150M camera was selected becausee of the enhanced NIR spectral characteristics of its IBIS5--AE-1300 image sensor (Sumix, Oceanside, CA, USA). It hass 1.3-mega pixel (1280 x 1024) sensor with high sensitivity inn 400 to 1000nm, increased light collection quantum efficienccy due to larger pixel size of 6.7 x 6.7µm, monochromaatic design, and extensive real time control and visualizatioon using its PC interface. The upper curve in Fig. 3 represennts the enhanced NIR efficiency of the sensor and the vertical ddashed line marks the 850nm wavelength of the LED light sourcce. The USB6009 hardware module from National Instrumennts (Austin, TX, USA) was used to trigger the light sources and the camera (USB-based digital and analog control and acquisition module). V. IMAGE ACQUISITION AND SYSTEM PERFORMANCE EVALUATIO ON The first image acquisition in ncluded trial 13 volunteers, under the auspices of the Institutional Research Board. The acquisition included multiple bursts of five image sequences, which were obtained from distancees of 0.75, 6 (Fig. 6), 7, 8, and 9 meters (Fig. 7). Backgroun nd NIR light was used for manual focus and a synchronized electronic e NIR “flash” was used for burst captures. In a second d trial, the eye images from an additional group of 15 volunteerss were acquired. Figure 3. The SMX-150M camera (left) and the spectraal charachteristics of its enhanced IBIS5-AE-1300 sensor (upper reed curve). For eye localization and gaze detectionn, we used the XUUK™ camera/light sources system (Fig. 4.a), which was originally designed to detect and count tthe human eyes looking at a target such as billboards. The appplication detects gaze based on the red-eye effect. We useed the provided development kit to write an application and reeport the relative coordinates of detected face/eyes to pan-annd-tilt module to point the mounted imaging system. We ussed PTU-D46-70 pan-and-tilt unit (Fig. 4.b) from Direccted Perception (Burlingame, CA, USA). We selected this parrticular model for its high-speed and accurate positioning of payyloads up to 9 lbs at speeds up to 60 degrees/second and positiion resolution of 0.013 degrees. Application developed in LabbVIEW (National Instruments) distributes the detected eyes cooordinates to the networked pan and tilt controller using TCP/IP P. Figure 5. Performance of the XUUK™ cam mera: no detection (a); detection of a person/face (b); direct gaze detection at about 1m distance (c); and detection of direct axial gaze at a distance off approximately 7.5m (d). Figure 6. Images acquired at 6 meters with hout (left) and with glasses (right). Figure 4. The Eyebox2 XUUK™ camera (a) and PTU--D46-70 module (b). Figure 7. Images acquired at 9 meters m standoff distance. Fig. 5 displays the functionality of the X XUUK™ system locating a person, his eyes, and presence of diirect gaze. In Fig. 5(a), the person is not detected. In 5(b), the face is detected and given an associated sequence number. Fiig. 5(c) indicates The number of pixels per iris diameter decreases proportionally with distance between the camera and subject. We noted that 6m distance, the combined c magnification of 147 lens-eyepiece optical system results in a field of view sufficient to image a single eye with more thaan 320 pixels per iris diameter. At 9m distance, the average iriss image diameter decreases to about 210 pixels. All images were stored for compuutational quality assessment and features extraction. A num mber of quality factors were implemented and used to selectt the best frames from a series of burst shots taken at distances of 1 to 9 meters. The images were first segmented and the low wer part of the iris region was used to compute the local quality m measures for best frame selection. Only correctly segmenteed frames were Different quality selected for computation of quality factors. D measures were tested, including: gradiennt-based metrics (Tenengrad, adaptive, separable and non-sepaarable Tenengrad, Laplacian, Adaptive Laplacian); correlation--based measures (autocorrelation function-single sample, area and height of central peak of the correlation function); statistics-based measures (absolute central moment, grey level variance, Chebytchev moments/ratios, entropy, histogrram); transformbased measures (Fourier transform: coefficiennts & magnitude, cosine transform, multivariate kurtosis, waveelets); and edgebased measures (step edge characteristics, transition width, local kurtosis) [34]. Focus quality assessmeent was used for best frame selection and to illustrate the qualiity variation with distance. Fig. 8 displays the best achieved qquality scores at different distances [35]. • Main camera lens has to have a large aperture for high photon collection and the image sensor needs high quantum collection efficiency at the wavelength of illumination (e.g. large pixel size); • The camera needs to haave high frame rate and electronically controlled settings; • Ambient light introducees degradation of image quality when NIR band-pass filter is not used. This is due to the different focal planes of differen nt wavelengths and/or glare and specular reflection; • Overall, the optical fron nt end has to have high magnification and quality (e.g. low w geometric distortion, fast, large aperture, high photon transfeer, and sufficient depth of field). It needs to be adapted for electronic focus, and preferably, including a fast and accurate assessment of subject distance; ynchronized with the NIR • The sensor has to be sy illumination sources considering intensity i levels within the exposure safety requirements; • Pan-and-tilt system has to have the ability to respond to reference coordinates with fasst vector movements and motion stabilization. Alternative an nd non-traditional pan and tilt mechanisms (e.g. arc mounted d or disk-based) could be better suited for this application to accommodate the lens and camera assembly and to operate witth less vibration; • A constellation of multiple distributed networked imaging cameras may cover largerr work volume and acquire better images based on control from one or more gaze detection systems. w calculated and used to A number of quality factors were select the best frames from a seriees of burst shots of each of the 28 subjects, taken from distan nces of 1, 6, 7, 8, and 9 meters. The results demonstrate the degradation of image quality with the increased stando off distances. Inter-subject comparison at the same distancee suggests that iris color assessment and selective spectral illumination/imaging may increase the image quality. This option is applicable to a design with multiple networked cameras and illumination sources. The illumination intensity lev vel and its bandwidth are critical. Light source’s spatial locaation and direction are also important. For example, the relatiive difference in the light source location is a probable cause for image quality differences in Fig. 8 for the left vs. right eyes. Axial alignment with the camera produces specular reflections from the cornea which may degrade performancce. On the other hand, especially if located on the pupil, the t specular reflection size could be used for focus adjustment and even PSF calculations. Future work will involve implementing a robust segmentation algorithm that would d perform better on images acquired from different distances, using u iris images at different scales, resolutions, locations and at various degradation levels. Implementation of fast voting criteeria for the selection of the best frame before and after segm mentation is critical for the performance of the system, and th hus further development of near real-time quality assessment is needed. Implementation of Figure 8. Best combined quality score for different stanndoff distances using our long-range aquision platform. VI. DISCUSSION AND CONCLUSSIONS In this paper we reported the design coonsiderations and implementation of a standoff image acquisition system for ocular biometrics. We used system integration of commercially available, off-the-shelf coomponents. We described important and often conflicting requuirements for the front end optical system. Regarding hardware, we concluded that rred-eye detection (e.g. by using XUUK™ system) can be succcessfully used to detect gaze direction from localized distant faace and eyes. Eye coordinates can be used to control a PTZ ssystem to reduce subject cooperation requirements. We concludde that: • The embedded computing power iss critical for the real time performance of the eye discoveery and tracking algorithm; 148 [17] T. Boult and W. Scheirer, “Long-Range Facial Image Acquisition and Quality” in Handbook of Remote Biometrics for Surveillance and Security, Edited by Massimo Tistarelli, Stan Z. Li and Rama Chellappa, Springer, pp. 169-192, 2009. [18] H. Proenca, “Non-cooperative iris recognition: Issues and Trends.”, 19th Europian Signal Processing Conference (EUSIPCO 2011), Barcelona, Spain, August29 – September 2, 2011. [19] H. Proenca, S. Filipe, R. Santos, J. Oliveira, and L. A. 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Tinker, ”Iris recognition in less constrained environments”, Springer Advances in Biometrics: Sensors, Algorithms and Systems, pp. 107–131, October, 2007. [34] H. Bharadwaj, H.S. Bhatt, M. Vatsa, and R. Singh, “Periocular biometrics: When iris recognition fails”. Fourth IEEE International Conference on Biometrics: Theory Applications and Systems (BTAS 10), Sept. 2010. [35] R. Derakhshani, P. Doynov, and B. Abidi, “An Acquisition Platform for Non-cooperative, Long Range Ocular Biometrics”, Project report, CITeR 2008. an integrated camera and illumination source feedback control in an embedded computational unit will make the system more robust and easier to use. Future work also needs to address illumination techniques (active, passive, and structured) for noncooperative standoff biometric systems. VII. ACKNOWLEDEMENTS This work was supported in part by a grant from the Center for Identification Technologies Research (CITeR). 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