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Institutionen för systemteknik Department of Electrical Engineering Examensarbete Iris recognition using standard cameras Examensarbete utfört i Bildkodning vid Tekniska högskolan i Linköping av Hans Holmberg

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Institutionen för systemteknik Department of Electrical Engineering Examensarbete Iris recognition using standard cameras Examensarbete utfört i Bildkodning vid Tekniska högskolan i Linköping av Hans Holmberg LITH-ISY-EX--06/3825--SE Linköping 2006 Department of Electrical Engineering Linköpings universitet SE Linköping, Sweden Linköpings tekniska högskola Linköpings universitet Linköping Iris recognition using standard cameras Examensarbete utfört i Bildkodning vid Tekniska högskolan i Linköping av Hans Holmberg LITH-ISY-EX--06/3825--SE Handledare: Examinator: Ingemar Ragnemalm isy, Linköpings universitet Ingemar Ragnemalm isy, Linköpings universitet Linköping, 18 September, 2006 Avdelning, Institution Division, Department Image Coding Group Department of Electrical Engineering Linköpings universitet S Linköping, Sweden Datum Date Språk Language Svenska/Swedish Engelska/English Rapporttyp Report category Licentiatavhandling Examensarbete C-uppsats D-uppsats Övrig rapport ISBN ISRN LITH-ISY-EX--06/3825--SE Serietitel och serienummer Title of series, numbering ISSN URL för elektronisk version Titel Title Irisigenkänning för standardkameror Iris recognition using standard cameras Författare Author Hans Holmberg Sammanfattning Abstract This master thesis evaluates the use of off-the-shelf standard cameras for biometric identification of the human iris. As demands on secure identification are constantly rising and as the human iris provides with a pattern that is excellent for identification, the use of inexpensive equipment could help iris recognition become a new standard in security systems. To test the performance of such a system a review of the current state of the research in the area was done and the most promising methods were chosen for evaluation. A test environment based on open source code was constructed to measure the performance of iris recognition methods, image quality and recognition rate. In this paper the image quality of a database consisting of images from a standard camera is assessed, the most important problem areas identified, and the overall recognition performance measured. Iris recognition methods found in literature are tested on this class of images. These together with newly developed methods show that a system using standard equipment can be constructed. Tests show that the performance of such a system is promising. Nyckelord Keywords iris recognition, biometric identification, image processing, computer vision Abstract This master thesis evaluates the use of off-the-shelf standard cameras for biometric identification of the human iris. As demands on secure identification are constantly rising and as the human iris provides with a pattern that is excellent for identification, the use of inexpensive equipment could help iris recognition become a new standard in security systems. To test the performance of such a system a review of the current state of the research in the area was done and the most promising methods were chosen for evaluation. A test environment based on open source code was constructed to measure the performance of iris recognition methods, image quality and recognition rate. In this paper the image quality of a database consisting of images from a standard camera is assessed, the most important problem areas identified, and the overall recognition performance measured. Iris recognition methods found in literature are tested on this class of images. These together with newly developed methods show that a system using standard equipment can be constructed. Tests show that the performance of such a system is promising. Acknowledgements I would like to thank Ingemar Ragnemalm for letting me realize my idea for this thesis, Adriana for support and love, my family for putting up with the initial iris imaging sessions, the creators of the UBIRIS and the CASIA iris image databases, Libor Masek for his open source code, god and house music. Portions of the research in this paper use the CASIA iris image database collected by Institute of Automation, Chinese Academy of Sciences. Notation This section introduces the notation and symbols used in this report. Symbols x, y, u, v Coordinates in R 2 r, θ Coordinates in 2D polar space σa 2 Standard deviation of the distribution a µ a Mean of the distribution a Operators and functions P P t X Y A B Complex conjugate of P Transpose of P Convolution of X and Y Binary exclusive or (xor) of A and B Acronyms IR Iris Recognition SNR Signal-to-Noise Ratio is a measure of image fidelity HD Hamming Distance as defined in section EER Equal Error Rate as defined in section 3.1 FAR False Acceptance Rate as defined in section 3.1 FRR False Rejection Rate as defined in section 3.1 Contents 1 Introduction Iris recognition using standard cameras The purpose of this report Document structure Problem description Resolution Occlusion Noise Reflections Compression Focus Light level Theory Biometric identification for IR Image processing for IR Edge detection The Canny operator Orientation in an image The Hough transform Iris recognition methods The IR process Segmentation Hough transform based methods Daugmans integro-differential operator Methods based on thresholding Normalization Mask generation Encoding and matching Daugmans method Other methods Proposed new methods Segmentation 4.6.2 Mask generation Evaluation The IrisLAB test system Iris image databases used The UBIRIS database The CASIA database Tests Encoding parameters Resolution Noise Reflection Occlusion Focus Light Compression Segmentation methods Mask methods Recognition performance Discussion Summary of results Fulfillment of goals Future work Bibliography 43 A Full test results 45 A.0.1 UBIRIS Encoding parameters A.0.2 Full resolution test results Chapter 1 Introduction Biometrics is, due to constant demands on higher security, an expanding field and the use of the human iris as a mean of identification has proved to be one of the most promising and secure methods. The iris is, due to its unique biological properties, exceptionally suited for identification; the iris is protected from the environment, stable over time, characteristic in shape and contains a high amount of discriminating information in its pattern. According to a survey [16] done by the National Physical Laboratory in the UK iris recognition (IR) outperforms other biometric identification methods (e.g. fingerprints, voice and face recognition) proving the technology to be the safest. Figure 1.1. An example of an iris from the UBIRIS database [20] 1.1 Iris recognition using standard cameras All iris recognition systems found in literature are based on specialized hardware, imaging the eye under favorable conditions. As imaging technology is rapidly becoming cheaper and the quality of off the shelf cameras is constantly rising, the idea behind this thesis is to look into the possibilities of making iris recognition an inexpensive and widespread technology using cheap imaging devices in less restrictive imaging situations. 1 2 Introduction When imaging the iris under less-than ideal conditions artifacts in the image occur such as different types of noise and reflections from light sources, artifacts that introduce errors in the iris recognition process, influence the performance and must be taken in consideration when designing IR systems. 1.2 The purpose of this report The purpose of this report is to evaluate the use of standard cameras for IR, localize and grade the problems that arise when imaging in non-ideal imaging situations using existing iris processing methods found in literature as well as newly developed methods based on general image processing methods. In short, the aim of this report is to answer the two questions: Is the image quality of standard equipment adequate for the purpose? Are there methods for IR robust enough to work under these circumstances? 1.3 Document structure First, a more in-depth problem description will be presented in chapter 2. Chapter 3 contains an introduction to image analysis and iris recognition concepts to provide with the theoretical background to the methods described in chapter 4. Chapter 5 describes the image databases used, the test system and the tests performed along the results. Finally, chapter 6 discusses the test results, the fulfillment of the goals and presents ideas for further research. Chapter 2 Problem description Imaging the iris is restricted due to the anatomical properties of the eye as well as noise introduced in the imaging situation. Eyelids together with eyelashes very often occlude a significant part of the iris, and this problem must be identified and handled in every robust iris recognition method. Also, when imaging eyes under less than perfect conditions, the resolution of the image might be insufficient and artifacts are inevitably introduced in the image as noise and blurring due to poor focus. This chapter will define and describe these problems for which solutions will be described in the methods in chapter 4 and the relative importance will be presented in section Resolution The spatial resolution of the image acquired of the iris is of course of great importance for the result of the iris recognition process. Different minimum resolutions have been presented. In [8] Daugman recommends that the iris radius should resolve a minimum of at least 70 pixels in radius and that typical systems use iris images with iris radiuses of 100 to 140 pixels but does not specify the exact iris image resolution used in his tests. Wildes suggest in [25] a lower radius limit of approximately 64 pixels to adequately discern details in the iris, based on an empirical estimate. 2.2 Occlusion The eyelids cover the eye to restrict light from entering into the eye when needed. This is a problem for IR when imaging the eye with visible light, as in the case is when using standard cameras. The problem can be solved by illuminating the eye with light outside the visible spectrum and has been done so in commercial applications. However, this thesis is about the application of standard camera equipment which operates in the visible spectrum. Therefore, this biological reaction must be handled. 3 4 Problem description Figure 2.1. Example of occlusion by eyelids and eyelashes from the CASIA database. Eyelid occlusion causes two problems, one in locating the eye in the image as it destroys the circular shape of the iris in the image and one in the template extraction process as the eyelid can cause a substantial part of the iris pattern to be covered and therefore invalid. Like the eyelids, eyelashes cause problems in both localization and in the template extraction, but to a lesser extent. Eyelashes are in comparison to the eyelids much harder to identify than eyelids due to their unstructured nature. 2.3 Noise Iris imaging is a type of measurement and all measurement are subjects to errors which can be modeled and handled as noise. One source of this noise is photon noise, arising as a natural part of light due to the fact that the number of photons hitting an image sensor and generating charge carriers is never exact and can only be described using probability. The noise in the imaging sensors and the surrounding electronics is often modeled as white and additive. 2.4 Reflections Figure 2.2. Example of reflections: (1) Reflections from the light source (2) Strong reflection from surroundings. (3) Weak reflections 2.5 Compression 5 The outermost part of the eye, the cornea, is a transparent layer that protects the eye and from which much of the light is reflected and causing a significant amount of specular reflection. Light sources and surrounding light areas such as windows can be seen projected on the surface of the eye. These reflections cause problems in the IR process, occluding the iris pattern and making the location of the eye harder to determine due to the fact that the reflections distort the annual shape of the eye. Three types of specular reflections can be identified as seen in figure High intensity reflections from light sources, causing total occlusion of underlying pattern. Often circular in shape and located inside the pupil boundaries. 2. High intensity reflections from surroundings i.e. windows, causing total occlusion of underlying pattern. Of arbitrary shape. 3. Low intensity reflections from surroundings. Lighting up the underlying pattern, but not causing complete occlusion. 2.5 Compression When saving the image data to a file, lossy compression is often used. This introduces information loss and can result in artifacts as visible image blocks and a loss of high frequency information in the iris pattern. 2.6 Focus Many digital imaging systems are restricted to a very limited depth-of-field because of requirements of low demands on lightning and exposure times. In short, imaging under low light and with short exposure times requires a high numerical aperture and this results in a low F-number that limits the depth-of-field. According to Plemmons[19] good focus is obtained in most IR systems through feedback to the user for correct alignment of the eye, which can be a significant obstacle and makes the systems less than fully automatic. When the eye is imaged outside the depth-of field, the image is blurred and results in poor recognition. If blurred, the iris boundaries become less sharp and results in poor localization, and the high frequency information in the iris pattern is lost which results in poor matching results. 2.7 Light level When imaging the iris under poor light, the signal to noise ratio decreases and along with image quality and the iris recognition rate decreases. In addition to that, poor light can result in a blurry image due to problems with the auto focus in the camera equipment. 6 Problem description The areas were problems are likely to occur when imaging the iris under nonideal conditions have now been presented. Methods to resolve the problems will be presented in chapter 4 and the respective importance of these problem areas will be evaluated in chapter 5. Chapter 3 Theory This chapter presents the concepts and theory needed for understanding the IR systems and methods presented in this thesis. Where the theory is too voluminous to fit in this thesis an introduction will be presented and pointers given to further reading. First an introduction to biometric identification concepts will be given followed by a presentation of different algorithms used in IR. 3.1 Biometric identification for IR All biometric identification between two samples of a chosen property of the human anatomy (fingerprints, iris, voice etc) results in a scalar value, indicating how similar the two samples are. For identification of two samples from the same person, this value should indicate great similarity and for samples from different persons this should result in great dissimilarity. For an ideal biometric technology, authentic matches (samples from the same person) would result in a dissimilarity value of zero, indicating that there is no difference between the samples and for impostor matches (samples from different persons) this would result in a dissimilarity value of one, indicating that the samples are completely different. However, there are no ideal biometric technologies, so dissimilarity values between one and zero will occur in the identification results. An example of this is showed in figure 3.1 which shows the resulting distributions of a set of authentic and impostor dissimilarity values. The distributions overlap, which makes it impossible to separate the two classes completely. Any chosen separation boundary, from now on named s, will give rise to either false rejections (two samples not determined to be from the same person) or false accepts (two samples from two different persons determined to be from the same person). 7 8 Theory Figure 3.1. Impostor and authentic match result distributions These errors are measured as FRR, false rejection rate as defined in equation 3.1, and FAR, false acceptance rate as defined in equation 3.2. The equal error rate measure, or ERR, is another performance measure that is defined at the point where FRR and FAR are equal. F RR = 1 s P authentics(x)dx 1 0 P authentics(x)dx (3.1) s 0 F AR = P impostors(x)dx 1 0 P impostors(x)dx (3.2) Another performance measurement is decidability, d, defined in equation 3.3 which measures the separation of the impostor and authentic distributions and takes in account the mean and standard deviations of the classes. The higher the decidability, the better separation between authentics and impostors and therefore better recognition performance. d = µ impostors µ authentics σ 2 impostors σ 2 authentics (3.3) Image processing for IR This section will give an introduction to image processing algorithms and concepts used in IR. This section does not aim to provide for a beginners course to image processing, but rather to explain the methods and algorithms used in this thesis. 3.2 Image processing for IR Edge detection Edge detection is a fundamental image analysis technique used for detecting object boundaries and data reduction in an image, see [5], preserving important structures while reducing the amount of data to process significantly. Edge detection is used in IR when locating the iris part of the image, see section 4.2. Edges in an image are defined as areas with sharp spatial intensity transitions. If looked upon as a continuous 2D function the edges would be the local maximums of the gradient. Assuming this, edge detection is then being reduced to finding max of the derivatives of the image intensity values I(u, v). Unfortunately, a digitized image is not a continuous function but rather a discrete function, so the derivatives must be approximated. As an image is a 2D signal the approximation of a derivative must be done in a defined direction. To estimate the derivative in any arbitrary direction, two estimates can be done d u and d v along the axes of the image and the derivative in the direction φ can be calculated according to: d φ (u, v) = cos(φ) d u (u, v) + sin(φ) d v (u, v) (3.4) The derivatives d u and d v are estimated through convolution of the image and derivative filters: d u = I h u d v = I h v (3.5) A number of different derivatives filters exist, with different noise reduction and frequency response characteristics. Two common filters: Sobel (see 3.7) and a basic filter pair(see 3.6) are shown below. The Sobel filter [23] is more insensitive to noise, while the basic one is very sensitive to noise but has a higher frequency range. h v = h u = [ ] (3.6) h v = h u = (3.7) Edge detection is used in IR when locating the iris in the image. See section The Canny operator The Canny operator [3] was designed to be an optimal edge detector, creating a binary edge map from an intensity image in a much more intelligent way than a mere thresholding of an edge strength estimate. The algorithm consist of three steps: 1. Noise reduction through convolution with a Gaussian filter. 10 Theory 2. Estimation of edge strength as described in section 3.2.1, using the Sobel filter pair. 3. Edge tracking is then performed along edges in the image starting in the positions in the image that have an edge strength bigger than a certain threshold (T 1 ). Tracking is continued, marking only local edge maximums as edges, until the edge strength falls below another threshold, (T 2 ). The use of the two thresholds aims to ensure that noisy edges does not break up into fragments and the Gaussian smoothing filter reduces the detectors sensitivity to noise. Increasing the size of the Gaussian filter makes the operator more insensitive to noise but reduces the ability to detect finer edges. The Canny operator is used in [17] and when locating the eyelids using the method described in Orientation in an image Images are two-dimensional signals, and as
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