Academic literature on the topic 'Iris biometric recognition'

The continued threat to security in our interconnected world today begs for urgent solution. Iris biometric like many other biometric systems provides an alternative solution to this lingering problem. Although, iris recognition have been extensively studied, it is nevertheless, not a fully solved problem which is the factor inhibiting its implementation in real world situations today. There exists three main problems facing the existing iris recognition systems: 1) lack of robustness of the algorithm to handle non-ideal iris images, 2) slow speed of the algorithm and 3) the applicability to the existing systems in real world situation. In this thesis, six novel approaches were derived and implemented to address these current limitation of existing iris recognition systems. A novel fast and accurate segmentation approach based on the combination of graph-cut optimization and active contour model is proposed to define the irregular boundaries of the iris in a hierarchical 2-level approach. In the first hierarchy, the approximate boundary of the pupil/iris is estimated using a method based on Hough’s transform for the pupil and adapted starburst algorithm for the iris. Subsequently, in the second hierarchy, the final irregular boundary of the pupil/iris is refined and segmented using graph-cut based active contour (GCBAC) model proposed in this work. The segmentation is performed in two levels, whereby the pupil is segmented first before the iris. In order to detect and eliminate noise and reflection artefacts which might introduce errors to the algorithm, a preprocessing technique based on adaptive weighted edge detection and high-pass filtering is used to detect reflections on the high intensity areas of the image while exemplar based image inpainting is used to eliminate the reflections. After the segmentation of the iris boundaries, a post-processing operation based on combination of block classification method and statistical prediction approach is used to detect any super-imposed occluding eyelashes/eyeshadows. The normalization of the iris image is achieved though the rubber sheet model. In the second stage, an approach based on construction of complex wavelet filters and rotation of the filters to the direction of the principal texture direction is used for the extraction of important iris information while a modified particle swam optimization (PSO) is used to select the most prominent iris features for iris encoding. Classification of the iriscode is performed using adaptive support vector machines (ASVM). Experimental results demonstrate that the proposed approach achieves accuracy of 98.99% and is computationally about 2 times faster than the best existing approach.

Personal identity refers to a set of attributes (e.g., name, social insurance number, etc.) that are associated with a person. Identity management is the process of creating, maintaining and destroying identities of individuals in a population. Biometric technologies are technologies developed to use statistical analysis of an individual’s biological or behavioral traits to determine his identity. Biometrics based authentication systems offer a reliable solution for identity management, because of their uniqueness, relative stability over time and security (among other reasons). Public acceptance of biometric systems will depend on their ability to ensure robustness, accuracy and security. Although robustness and accuracy of such systems are rapidly improving, there still remain some issues of security and balancing it with privacy. While the uniqueness of biometric traits offers a convenient and reliable means of identification, it also poses the risk of unauthorized cross-referencing among databases using the same biometric trait. There is also a high risk in case of a biometric database being compromised, since it’s not possible to revoke the biometric trait and re-issue a new one as is the case with passwords and smart keys. This unique attribute of biometric based authentication system poses a challenge that might slow down public acceptance and the use of biometrics for authentication purposes in large scale applications. In this research we investigate the vulnerabilities of biometric systems focusing on template security in iris-based biometric recognition systems. The iris has been well studied for authentication purposes and has been proven accurate in large scale applications in several airports and border crossings around the world. The most widely accepted iris recognition systems are based on Daugman’s model that creates a binary iris template. In this research we develop different systems using watermarking, bio-cryptography as well as feature transformation to achieve revocability and security of binary templates in iris based biometric authentication systems, while maintaining the performance that enables widespread application of these systems. All algorithms developed in this research are applicable on already existing biometric authentication systems and do not require redesign of these existing, well established iris-based authentication systems that use binary templates.

Thesis (M.S.)--West Virginia University, 2007.<br>Title from document title page. Document formatted into pages; contains x, 93 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 86-90).

<p>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.</p><p>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.</p>

The Internet has grown from its modest academic beginnings into an important, global communication medium. It has become a significant, intrinsic part of our lives, how we distribute information and how we transact. It is used for a variety of purposes, including: banking; home shopping; commercial trade - using EDI (Electronic Data Interchange); and to gather information for market research and other activities. Owing to its academic origins, the early developers of the Internet did not focus on security. However, now that it has rapidly evolved into an extensively used, global commercial transaction and distribution channel, security has become a big concern. Fortunately, the field of information security has started to evolve in response and is fast becoming an important discipline with a sound theoretical basis. The discipline views the twin processes of identification and authentication as crucial aspects of information security. An individual access attempt must be identifiable prior to access being authorised otherwise system confidentiality cannot be enforced nor integrity safeguarded. Similarly, non-denial becomes impossible to instigate since the system is unable to log an identity against specific transactions. Consequently, identification and authentication should always be viewed as the first step to successfully enforcing information security. The process of identification and authorisation is, in essence, the ability to prove or verify an identity. This is usually accomplished using either one or a combination of the following three traditional identification techniques: something you possess; something you know; or something you are. A critical consideration when designing an application is which identification method, or combination of methods, from the three described above to use. Each method offers its own pros and cons and there are many ways to compare and contrast them. The comparison made in this study identifies biometrics as the best solution in a distributed application environment. There are, however, two over-arching hindrances to its widespread adoption. The first is the environment’s complexity - with multiple applications being accessed by both the public and the private sectors - and the second is that not all biometrics are popular and no single method has universe appeal. The more significant hindrance of the two is the latter, that of acceptance and trust, because it matters little how good or efficient a system is if nobody is willing to use it. This observation suggests that the identification system needs to be made as flexible as possible. In a democratic society, it could be argued that the best way of ensuring the successful adoption of a biometric system would be to allow maximum freedom of choice and let users decide which biometric method they would like to use. Although this approach is likely to go a long way towards solving the acceptance issue, it increases the complexity of the environment significantly. This study attempts to solve this problem by reducing the environment’s complexity while simultaneously ensuring the user retains maximum biometric freedom of choice. This can be achieved by creating a number of central biometric repositories. Each repository would be responsible for maintaining a biometric template data store for a type of biometric. These repositories or “Biometric Authorities” would act as authentication facilitators for a wide variety of applications and free them from that responsibility.<br>Dissertation (MSc (Computer Engineering))--University of Pretoria, 2007.<br>Electrical, Electronic and Computer Engineering<br>MSc<br>unrestricted

Security breaches due to misidentification of an individual pose one of the greatest threats and challenges for today’s world. The use of biometrics can be quite promising in minimising this threat. Biometrics refers to the automatic authentication of individuals based on their physiological and behavioural characteristics. To date, various biometric systems have been proposed in the literature, among them biometric traits such as the face, iris, fingerprints, retina, gait, and vocal patterns are found to be distinctive to each and every person and are considered to be most reliable biometric identifiers. Regardless of the available biometrics traits, to date, no biometric system has been found to be a perfect, and which can be applied universally in a way that is robust/adaptive to change in different environmental conditions. Multimodal biometric systems were proposed in the late 1990’s to extend the range of biometric applicability. In a multimodal biometric system, two or more biometric identifiers are fused by an information fusion technique, thereby providing robustness for changing in a greater range of environmental conditions and enhancing other properties that an ideal biometric system should possess. Another important property that a biometric system should possess is a capability to distinguish between real and fake data. Although both the robustness of the system and capability to distinguish between a real and fake data should be incorporated into a single system, there is a trade-off. Therefore, due to the aforementioned research problems, this thesis addresses advancements in multimodal ocular biometrics using iris and sclera and also investigates the trade-off between robustness/adaptability and anti-spoofing/liveness detection (which is one method to distinguish between real and fake data). Biometrics traits that allow personal identification, eye traits offer a good choice of biometrics, as the eye offers a wide range of unique characteristics. The two common eye biometric identifiers that can be found in the literature are the iris and retina. Two more biometrics that are becoming popular nowadays are the sclera and the peri-ocular. The iris biometric is believed to be the most reliable eye biometric and that is why various commercial products based on this biometric are available; but the iris biometric used in an unconstrained scenario is still an open research area. The performance of iris biometrics with changes in the gaze angle of the eye can be affected highly. Therefore, due to this restriction, high user cooperation is required by persons with squinty eyes to get successfully identified in an iris biometric system. Identifying individuals with darker irises is another big challenge in iris recognition in the visible spectrum. To mitigate this problem, multi-modal eye biometrics was proposed by combining iris and sclera traits in the visible spectrum. However, in order to establish the concept of multimodal eye biometrics using the iris and sclera, it is first necessary to assess if sufficient discriminatory information can be gained from the sclera, further assessment in regards to its combination with the iris pattern and adaptiveness of the traits with respect to changes in environmental conditions, population, the data acquisition technique and time span. Multimodal biometrics using sclera and iris have not been extensively studied and little is known regarding their usefulness. So, the state-of-the-art related to it is not sufficiently mature and still in its infancy. This thesis concentrates on designing an image processing and pattern recognition module for evaluating the potential of the scleral biometric with regards to biometric accuracy. Thus, research is also carried out investigate usefulness of the sclera trait in combination with the iris pattern. Various, pre-processing techniques, segmentation, feature extraction, information fusion and classification techniques are employed to push the border of this multimodal biometrics. The latter half of the thesis concentrates on bridging the anti-spoofing technique liveliness with adaptiveness of biometrics. Traditional biometric systems are not equipped to distinguish between fake and real data that has been scanned in front of the sensors. As a result, they adhere to forgery attacks by intruders who can take the privilege of a genuine user. With the rising demand of involuntary or unmanned biometric systems in border security, flight checking, and other restricted zones, the incorporation of the automatic detection of forgery attacks is becoming very obvious. Adaptability of the system with respect to the change in the trait is another important aspect that this biometric system should be enriched with. As mentioned previously both the forgery detection method (termed as liveness detection in the literature of biometrics) and adaptability of the trait is necessary for a trusted involuntary biometric system, but initial studies in the literature exhibit it as a trade-off. Therefore to fulfil the gap, this thesis aimed to propose a new framework for software-based liveness detection, which is also associated to the adaptability of the trait. To fulfil the above-highlighted aim in the proposed framework, intra-class level (i.e. user level) liveness detection is introduced employing image quality-based features. Furthermore, to incorporate the adaptability of the trait, online learning-based classifiers are used. Initial investigation and experimental results solicit the use of the proposed framework for trusted involuntary biometric systems. Two new multi-angle eye datasets were developed and published as a part of the current research. The thesis also consists of contributions to other fields of pattern recognition such as wrist vein biometrics, multiscript signature verification and script identification.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Info & Comm Tech<br>Science, Environment, Engineering and Technology<br>Full Text

Our purpose with this thesis was to find biometric methods that can be used in access control of mobile access. The access control has two parts. Firstly, to validate the identity of the caller and, secondly, to ensure the validated user is not changed during the session that follows. Any solution to the access control problem is not available today, which means that anyone can get access to the mobile phone and the Internet. Therefore we have researched after a solution that can solve this problem but also on how to secure that no one else can take over an already validated session. We began to search for biometric methods that are available today to find them that would be best suited together with a mobile phone. After we had read information about them we did choose three methods for further investigation. These methods were Fingerprint Recognition, Iris Scan and Speaker Verification. Iris Scan is the method that is best suited to solve the authentication problem. The reasons for this are many. One of them is the uniqueness and stability of the iris, not even identical twins or the pair of the same individual has the same iris minutiae. The iris is also very protected behind eyelids, cornea and the aqueous humor and therefore difficult to damage. When it comes to the method itself, is it one of the most secure methods available today. One of the reasons for this is that the equal error rate is better than one in a million. However, this rate can be even better. It all depends on the Hamming Distance, which is a value that show how different the saved and temporarily template are, and what it is set to. To solve our session authentication, which was to make sure that no one else could take over a connected mobile phone, a sensor plate is the answer. This sensor will be able to sense for touch, heat and pulse. These three sensor measurements will together secure a validated session since the mobile phone will disconnect if the sensor looses its sensor data. There are, however, technological and other challenges to be solved before our proposed solutions will become viable. We address some of these issues in our thesis.

Biometric systems allow identification of human persons based on physiological or behavioral characteristics, such as voice, handprint, iris or facial characteristics. The use of face and iris recognition as a way to authenticate user’s identities has been a topic of research for years. Present iris recognition systems require that subjects stand close (<2m) to the imaging camera and look for a period of about three seconds until the data are captured. This cooperative behavior is required in order to capture quality images for accurate recognition. This will eventually restrict the amount of practical applications where iris recognition can be applied, especially in an uncontrolled environment where subjects are not expected to cooperate such as criminals and terrorists, for example. For this reason, this thesis develops a collection of methods to deal with low quality face and iris images and that can be applied for face and iris recognition in a non-cooperative environment. This thesis makes the following main contributions: I. For eye and face tracking in low quality images, a new robust method is developed. The proposed system consists of three parts: face localization, eye detection and eye tracking. This is accomplished using traditional image-based passive techniques such as shape information of the eye and active based methods which exploit the spectral properties of the pupil under IR illumination. The developed method is also tested on underexposed images where the subject shows large head movements. II. For iris recognition, a new technique is developed for accurate iris segmentation in low quality images where a major portion of the iris is occluded. Most existing methods perform generally quite well but tend to overestimate the occluded regions, and thus lose iris information that could be used for identification. This information loss is potentially important in the covert surveillance applications we consider in this thesis. Once the iris region is properly segmented using the developed method, the biometric feature information is calculated for the iris region using the relative entropy technique. Iris biometric feature information is calculated using two different feature decomposition algorithms based on Principal Component Analysis (PCA) and Independent Component Analysis (ICA). III. For face recognition, a new approach is developed to measure biometric feature information and the changes in biometric sample quality resulting from image degradations. A definition of biometric feature information is introduced and an algorithm to measure it proposed, based on a set of population and individual biometric features, as measured by a biometric algorithm under test. Examples of its application were shown for two different face recognition algorithms based on PCA (Eigenface) and Fisher Linear Discriminant (FLD) feature decompositions.

A textura da íris humana é uma das peculiaridades biométricas mais confiáveis, pois os padrões que compõem sua estrutura são considerados únicos e estáveis por longos anos. No entanto, amostras de íris capturadas em ambiente não cooperativo como reconhecimento de íris a distância, por exemplo, estão sujeitas a conter variações na textura, devido a mudanças comportamentais da membrana da íris. Outro problema é a complexidade do algoritmo, que o torna inviável para aplicações práticas ou em tempo real. O objetivo deste trabalho foi avaliar alguns descritores de textura locais para o reconhecimento biométrico de íris, considerando os efeitos de dilatação e contração da pupila. Para a comprovação da hipótese desta tese de doutoramento, foi utilizada uma base de dados contendo amostras de íris com a pupila contraída e dilatada, simulando assim, a aquisição natural em ambiente não cooperativo. Além disso, foram propostos dois novos descritores, denominados como Median Local Mapped Pattern (Median-LMP) e Modified Median Local Mapped Pattern (MM-LMP), que foram comparados com o método de Daugman, o Local Mapped Pattern (LMP), o Completed Modeling of Local Binary Pattern (CLBP), o Median Binary Pattern (MBP) e o Weber Law Descriptor (WLD). Os resultados da avaliação de desempenho mostraram que o algoritmo de Daugman é o melhor para o reconhecimento de íris quando é realizada a comparação entre amostras de íris com pupilas contraídas. No entanto, se a pupila está dilatada, os descritores propostos apresentaram o melhor desempenho, principalmente se uma amostra de íris com uma pupila contraída é comparada com outra íris com a pupila dilatada. Além disso, os descritores propostos e o LMP obtiveram os menores tempos de processamento, sendo mais adequados do que os demais para aplicações em tempo preditivo com implementação em hardware.<br>The texture of the human iris is one of the most reliable biometric traits, so the patterns that make up its structure are the only criteria and stable for long time. However, iris samples captured in a noncooperative environment as recognition of nature, for example, subject to contain variations in texture, due to behavioral changes of the iris membrane. Another problem is an algorithm complexity, which makes it impractical for practical or in real-time applications. The objective of this work is to evaluate some local texture descriptors for the biometric iris recognition, considering the effects of dilation and contraction of the pupil. In order to prove the hypothesis of this doctoral question, a database was used containing iris samples with a contracted and dilated pupil, thus simulating a natural acquisition in a noncooperative environment. In addition, two new descriptors, named Median-Local Standard Mapped (Median-LMP) and Modified Modified Local Standard Mapped (MM-LMP) were proposed, which were compared with the Daugman method, the Mapped Local Pattern (LMP), the Complete Local Binary Pattern Modeling (CLBP), the Median Binary Standard (MBP) and Weber Law Descriptor (WLD). The results of the performance evaluation show that the Daugman algorithm is the best for iris recognition when a study of iris samples with the students is performed. However, if a pupil is dilated, the proposed descriptors show the best performance, especially a sample of iris with a contracted pupil is compared to another iris with a dilated pupil. In addition, the proposed descriptors and the LMP obtained the shortest processing times, being more adequate than the others for predictive time applications with hardware implementation.