The Use of Biometrics and Bio-Information to Support New Systems Integration - Research Paper Example

THE USE OF BIOMETRICS AND BIO-INFORMATION TO SUPPORT NEW SYSTEMS INTEGRATION The use of Biometrics and Bio-information to Support New Systems Integration Author Author’s Affiliation Date Table of Contents Introduction 3 Biometric Systems and Applications 5 Biometric Technology Areas 7 Why Biometrics and Bio-information Integration? 9 How to Integrate Information? 12 Large Scale Systems Integration Applications 14 Transforming and integrating your biometrics capability 17 Issues in Integrating Biometrics 18 Integrated Biometric Technology Development 19 Device Capabilities 19 Issues in Development 20 Support Issues 20 Conclusions 20 References 22 Abstract In the past few years there have emerged a large number of security threats from different domains. In fact, a large number of terrorist attacks have caused serious influences in every walk of life. In this scenario, different private and government organizations use a wide variety of security techniques to ensure the security of the people. Additionally, biometrics is one of the most commonly used security devices. A biometric device identifies people on the basis of their physical characteristics such as their eyes, fingers, hands and so on. There are many kinds of biometrics and they all follow the same principles. In addition, they are very useful in many cases. For instance, they automate the process of employees’ attendance. This paper presents an analysis of biometrics systems. The basic purpose of this research is to determine and analyze the use and role of biometrics and bio-information to support new Systems integration. Introduction Existing techniques to ensure security and human identification which are based on deriving credentials through PIN and identification credentials are not capable to deal with the increasing requirements for strict security measures in implementation of border crossings, national ID cards, access control and government profits. In this scenario, biometric recognition or simply biometrics systems, which are based on human identification through their behavioral and physiological features, are being more and more implemented and mapped to quickly growing human recognition tools and applications. Without a doubt, this technology is much better than various documents based identification, for the reason that biometric traits (for example face, fingerprint and iris) cannot be stolen, lost or simply forged. In fact, these systems are believed to be more and more stable and distinctive. On the other hand, the use of biometrics systems and techniques is not new; fingerprints based reorganization have been effectively utilized for over one hundred years by law and forensics enforcement agencies with the purpose of identifying and arresting criminals. The research has shown that biometric systems provide generally secure and suitable authentication mechanism. In addition, biometrics systems identify a person on the basis of their exceptional behavioral or physical features. In this scenario, some of the commonly used physical features can comprise hand, fingerprints or retina, palm geometry iris, and facial features while behavioral features encompass voice, gait, signature and keystroke. In view of the fact that these characteristics of a person cannot be stolen, borrowed or elapsed and falsify hence these systems are believed to be more realistic and useful (Mordini & Tzovaras, 2012; Jain & Kumar, 2010; Kumar & Prathyusha, 2009). In addition, a biometric system is based on an automatic process initiated to identify people based on their physiological or behavioral features. In this scenario, latest biometric systems carry out this process by taking into consideration a single source of biometric data and are influenced by various issues such as non-uniqueness, noisy sensor data and lack of independence of the selection of the biometric attribute, lack of an invariant illustration of the biometric feature as well as vulnerability to circumvent. In fact, the majority of these issues can be resolved and this process can be improved by making effective use of state-of-the-art integrated biometric systems that include verification from numerous biometric sources. In this scenario, integration of authentications acquired from various sources is a challenging task and integrating those authentications at the identical score level is a very useful method for the reason that it presents the most excellent trade-off among information content and the easiness in integration (Nandakumar, 2005). This paper will present an analysis of biometric systems and bio-information. The basic purpose of this research is to identify the role and use of biometrics and bio-information to support new systems integration. This research basically attempts to discover how biometrics and bio-information technology will help and support new systems integration. Biometric Systems and Applications There is a wide variety of biometric systems and technologies that can be implemented to support different kinds of environments and organizations. In this scenario, the selection of an appropriate biometric system is a critical process which requires the organizations to map through several multifaceted vendor products and keep an eye on developments and advancements in this area. Given below are some of the well-known techniques used by biometric systems: One of the most important techniques used by biometric systems is identification through fingerprints detection, which appear in the patterns discovered on a fingertip. However, there are a number of methods for fingerprint authentication for example as customary police technique, with pattern-matching systems or tools, and things similar to more border patterns and ultra-sonics. Hence, this technique can be an excellent choice for in-house systems (PeterIndia, 2013; Nandakumar, 2005). There is another useful technique that is known as hand geometry, which is aimed analyzing and determining the structure and shape of the hand. However, it can be appropriate for those organizations that have to deal with a large number of clients or where the client access the system occasionally. Additionally, a large number of organizations are already making use of hand geometry readers in a number of ways, such as for attendance and time recording (PeterIndia, 2013; Nandakumar, 2005). There is another very useful identification technique through which a biometric system identifies a person on the basis of the retina. This process involves assessing the layer of blood vessels located at the back of the human eye. In addition, this method basically involves making use of a low intensity light source by means of an optical coupler to scan the distinctive eye blood vessel patterns of the eye’s retina. Additionally, in many cases retinal scanning can be quite perfect however does necessitate the user to look into a receptacle as well as focus on a particular point in the system, hence it requires a lot of the user’s attention (PeterIndia, 2013; Nandakumar, 2005). In the same way, some biometric systems use Iris based to assess characteristics discovered in color eye ring of tissue of humans that surrounds the pupil. In this scenario, this process is completed using a reasonable standard camera component which necessitates no close contact between the client and reader. In addition, these systems are more powerful in authentication than standard template matching competence (PeterIndia, 2013; Nandakumar, 2005). Face recognition based system authenticate humans by identifying their facial features. In this scenario, they make use of a digital camera to catch a facial image of the client for further processing. In view of the fact that these systems require additional peripheral equipments that are not incorporated in fundamental personal computers, hence they are useful for network verification. In addition, the casino business has capitalized this technology to produce a human’s facial database of scam artists for rapid recognition of security staff (PeterIndia, 2013; Nandakumar, 2005). Some biometric systems authenticate a person by determining the way a person signs his name. In this scenario, signing features like that velocity, speed and pressure are as significant as the complete signatures fixed shape. In addition, people are required to provide their signatures with transaction associated characteristics confirmation (PeterIndia, 2013; Nandakumar, 2005). Another most important recognition technique is known as voice recognition. These systems are used on voice-to-print verification, where complex technology changes voice into text. In this scenario, voice recognition biometric systems require a microphone that comes to personal computers these days. It is expected that voice biometrics can substitute the presently used techniques, for example passwords, PINs or account names. However, voice will be a matching technique for finger-scan systems as a large number of people notice finger scanning like a superior verification form (Al-Hijaili & AbdulAziz, 2011; PeterIndia, 2013). Biometric Technology Areas There are four technological developments that will direct development of 2nd generation biometrics technology based systems, these development areas are outlined below: 1. Appearance of potentially useful up-to-date biometric qualities 2. Additional value offered by soft biometrics 3. Efficient use of a wide variety of biometric traits with the purpose of large-scale human identification 4. Technologies to make sure an elevated degree of confidentiality, safety and flexibility in the area of biometrics systems In addition, possibilities and issues for the next generation biometrics systems and technologies are enormous. In this scenario, the assurance of effective growth of 2nd generation biometric systems will require collective and permanent effort, before resulting from a particular innovative invention. In some cases low rate of biometric sensors and satisfactory matching performance have been dominant aspects of the status of fingerprint modality for viable practice. Additionally, ever-increasing developments in corresponding performance as well as a steady decrease in price of biometric sensors can modify the collection of biometric modalities in the future. In fact, the development of smart sensing systems and technologies will allow developers and researchers to successfully make use of comprehensive biometric characteristics as well as build-up high performance matchers through well-organized noise exclusion methods. Similar extensive efforts are able to attain a great deal desired gain from the 2nd generation biometrics technologies at a quicker pace. Without a doubt, the need for effective identification and authentication tools for security reasons is not limited to homeland security and security agencies. In fact, other private and public entities also require these systems, such as health care suppliers and banks. In addition, the implementation of effective security mechanisms has become a challenge in the present day environment. Additionally, pressures on financial plans as well as partial resources require earlier and more exact performance of necessary tasks. In fact, this is more pertinent than ever in light of the growing amount of travelers and transactions as the worldwide population carry-on to grow. Some of the critical areas of biometric systems implementation include the processing of asylum systems, even the flow of cross-border traffic, verification of criminal identity and controlled access to the armed services, health records and even bank accounts. In the same way, a large number of businesses are adopting and implementing biometric systems and technologies. It is an admitted fact that biometric systems maintain the key to superior competence, correctness and speed. Normally focused on fingerprints, the constant drive for improved accuracy while handling higher transaction volumes is creating a development in the direction of the multi-modal biometric systems and application (Jain & Kumar, 2010; Groupe Steria SCA, 2012). Why Biometrics and Bio-information Integration? As discussed above, uni-modal based biometric systems carry out the process of human verification and authentication on a single source of biometric or available information. Similar technology based systems are frequently influenced by the some of the major issues, outlined below: First of the most important issues is noisy sensor data, which can cause serious problems for facial detection. In this scenario, there would be some noise in obtained biometric data primarily because of faulty or indecently handled detection sensors. For instance, buildup of dirt or the residual remains on a finger-print sensor can create some noisy finger-print image. In the same way, not focusing on the camera suitably can also lead to blurring in iris or face images. The detection precision of a biometric technology based system that is extremely sensitive to the quality of the biometric input as well as noisy data can also result in a decrease in the accurateness of the biometric detection system. In some cases, non-uniqueness is also one of the major issues in the detection and authentication process, where each person in the target population is capable to offer the biometric trait intended for detection, and then the trait is identified as unique. In view of the fact that biometric systems identify a person on the basis of their unique characteristics hence this uniqueness is one of the fundamental needs for a biometric identifier based system. On the other hand, the research has shown that not all biometric qualities are in fact common. In this scenario, National Institute of Standards and Technology (NIST) has outlined that it is not feasible to get a high-quality finger-print from about 2% of the population. Therefore, people with similar characteristics cannot be registered in a fingerprint authentication system. In the same way, people with long eyelashes and those who have some kind of eye based irregularity or diseases similar to aniridia, glaucoma, cataract or nystagmus are not able to offer high-quality iris pictures used by automatic biometric systems. In addition, non-uniqueness takes to failure to capture or some kind of failure to enroll issues in a biometric detection based system. In fact, lack of unique features is also one of the major issues in detection processes. For instance, appearance supported facial characteristics that are usually used in the majority of present face recognition systems are discovered to have partial biased ability. In this scenario, some part of the population can also have almost matching facial appearance because of genetic characteristics (for instance identical twins, father and son). This lack of distinctiveness increases the FMR (False Match Rate) of a biometric system. Additionally, the biometric data obtained from a person all-through the process of authentication will not be matching to the data employed in producing the person’s template all through the enrollment and this situation is acknowledged as intra class dissimilarity. Actually, these differences can emerge as a result of inappropriate communication of client with the sensor (for example making changes because of translation, rotation and implemented pressure when the client places his finger on a finger-print sensor, variations in pose and appearance when the client stands in front of a camera, etc.), use of dissimilar sensors all through the employment as well as authentication, transformations in the ambient environmental circumstances (for example lighting transformation in a face detection system) and intrinsic transformations in the biometric attribute (for example outside the lines because of presence of facial hair or aging changes in face images, existence of scars in a finger-print, etc.). If at all possible, the characteristics taken out of the biometric data have to be comparatively invariant to these transformations. Though, in the majority of realistic biometric technology based systems the characteristics are not invariant as well as consequently complex matching algorithms are implemented to deal with these dissimilarities. In fact, heavy intra class differences typically augment the False Non-Match Rate (FNMR) of a biometric technology based system. Vulnerability to circumvention is also one of the most important aspects to consider when dealing with biometric systems. In view of the fact that it is not an easy task to extract someone’s biometric behavior, however it is yet probable for an impostor to keep away from a biometric system through spoofed behavior. The research has shown that it is possible to build gummy fingers by making use of growing fingerprint imitations and make use of them to deceive a biometric system. In this scenario, behavioral qualities especially signature and voice are more vulnerable to similar security attacks as compared to physiological behavior. In view of the fact that these systems can have numerous issues hence the fault rates connected with uni-modal biometric technology based systems are fairly high that makes them intolerable for use in protection and safety critical systems. In this scenario, the majority of such issues that influence the capabilities of uni-modal biometric systems can be resolved through integrated biometric technology based systems. Additionally, biometric systems that combine signals retrieved from two or more biometric technology based sources with the purpose of person detection are known as integrated biometric systems. In fact, the latest technology based integrated biometric systems have numerous benefits over uni-modal systems. In addition, through integration of biometrics and bio-information can considerably improve the general correctness of biometric detection system. As discussed above, integrated biometric systems are able to decrease the FTE/FTC rates and offer a great of resistance besides spoofing for the reason that it is hard to all together spoof a number of biometric detections and authentication sources. Additionally, integrated systems are able to offer the potential to investigate a huge database in a well-organized and fast way. This is able to be attained through by means of a comparatively straightforward however less precise modality to prune the database previous to employing the more composite and precise modality on the continuing data to carry out the ultimate authentication job. Though, integrated biometric systems as well have a number of issues for instance, they are more costly as well as demand more resources for processing and storage as compared to uni-modal biometric technology based systems. In the same way, integrated technology based systems normally require more time for authentication and enrollment analysis which causes several problems for the client. Though, the benefits of integrated technology based systems far balance the limits and therefore, these systems are being more and more applied in security based significant implementations (Nandakumar, 2005; Gokulkumari & Lakshmi, 2011; Han, 2011). How to Integrate Information? The design of an integrated technology based biometric system completely depends on the system working and operational situation. Up till now, many researchers have discussed different kinds of integrations and integrated biometric systems. These systems are different from another with respect to their working arrangement, the amount and preference of biometric modalities, the intensity at which the proof is collected and the techniques employed for the incorporation or synthesis of data. Basically, there are four levels of integration in an integrated biometric system. These levels include integration at the sensor level, characteristic mining level, corresponding score level and decision making level. In this scenario, sensor level integration is quite rare for the reason that integration at this point needs that the information should be attained from diverse biometric sensors and it has to be well-matched. In the same way, integration at the characteristic level is as well not for all time appropriate for the reason that the characteristic sets employed by diverse biometric modalities can either be unreachable or mismatched. In addition, integration at the system detection decision level is too inflexible hence simply a partial amount of bio-details can be retrieved. In this scenario, integration at the identical score level is normally preferred over other mechanisms because of the occurrence of enough data content as well as the easiness in accessing as well as joining corresponding scores. In addition, there are two ways to accomplish integration at the corresponding score level during the process of authentication. The first technique takes into consideration the classification complexity, and second way takes into consideration the implementation complexity. In the detection classification method, a characteristic vector is created by means of identical scores results through the individual matchers; this characteristic vector is then categorized into one of two classes: Reject (impostor) or “Accept” (genuine user). In this process, the particular corresponding scores are combined to produce a single scalar score that is then employed to formulate the ultimate decision. In addition, past researches have extensively spotlighted on these methods. The research has shown that the combination of these methods shows much better results than a number of classification techniques similar to decision tree and linear discriminate analysis. Though, in reality that no particular classification or grouping method which works effectively under all circumstances (Nandakumar, 2005; Stiglic, Mertik, Kokol, & Pighin, 2007; Khan, Farhan, Khurshid, & Akram, 2011). Large Scale Systems Integration Applications Biometric technology based systems are able to successfully and powerfully operate in ultra large-scale (ULS) systems, for example they can support the organizations that have hundreds of millions of registered clients, and have a number of possible prospects. Similar technology based systems will be capable to facilitate National-ID plans or develop homeland safety, e-commerce, and extra efficient application of social well-being planes in nations with huge inhabitants (for example China, India and US). The prospects for biometric technology based systems for similar huge-scale systems can be summarized as follows: High accuracy and efficiency under various changing operating circumstances as well as user scenarios Sensor interoperability Fast and accurate set of biometric data in harsh functioning situations with virtually no failure to enroll rate Assurance of maximum security with high levels of confidentiality and template safety Secure support for operating systems or information The four major well-known biometric technology based modalities established nowadays are fingerprint, face, hand geometry and iris. The initial generation biometric technology based systems normally established a single modality, mainly fingerprint (ten prints), intended for huge-scale systems. As the automated border crossing in a number of regions (for example USA and Japan) is supported via verification through fingerprints, the new technology based biometric system in the UK is established on iris biometric verification and the one in Australia is based on face detection. In this scenario, the US secret agency FBI has broadened on its NGI or New Generation Identification project for law management and enforcement systems that will combine face, fingerprints and palm-prints along with a number of soft biometrics similar to marks, scars and tattoos (SMT). In the same way, the national administration of India has recently publicized an innovative project, which involves the implementation of a biometric systems acknowledged as Unique ID to offer flexible and distinctive recognition number to its over one billion people. In fact, it is expected that the project will generate the biggest biometric database on the planet as well as on its successful implementation; it is able to turn out to be a model of extremely huge-scale use of biometrics in electronic governance. In addition, it is normally supposed that the recovery of biometric patterns from the database of India’s billion and inhabitants will need extremely competent indexing methods for biometric data. Consequently, the design as well as development of well-organized and efficient huge-scale indexing methods for the multi biometric data is one more challenge in the proficient use of huge-scale biometric technology based systems. In fact, the independence or the attainable acknowledgment performance from the selected biometric modality is another significant principle when millions of individualities require being distinguished. In this scenario, there are chances of occurrence of matching twins that is a serious issue which needs to be carefully considered in such a huge-scale implementation. However, integrated biometric systems that are able to concurrently make use of a large number of biometric modalities (for example numerous two irises, fingers, iris and finger, etc.) are predictable to present superior precision and are able to deal with the issues of non-uniqueness. Additionally, biometrics technology based systems that are able to concurrently obtain a large number of modalities are likely to turn out to be well-liked in huge-scale applications through data gathered in the field (Jain & Kumar, 2010; Nandakumar, 2005; Turinsky, Ah-Seng, Stromer, Taschuk, Xu, & Sensen, 2005). Moreover, a new generation of access control systems is on board that is identified as the EAC (Extended Access Control) that will be handled through the ePassports that is a new generation, safer, and protected MRTD (machine-readable travel documents). As present Basic Access Control (BAC) ePassports are in production in a lot of regions of world, here Basic Access Control is the "next generation" of ePassports, facilitating the validity that simply allows bodies those are capable to enter through the biometric access. This can be involving the fingerprint scene or iris scans that are stored on the contactless chip inside an ePassport. However, through the future European Union obligation for Extended Access Control was released in June 2009. In this scenario administrations are developing their ePassport plans. Assign, a leader in ePassport safety modernism, has urbanized a PKI (public key infrastructure) to strongly facilitate administrations that are developing to Extended Access Control-based ePassport security arrangement (Ghaboosi, 2009). In these kinds of more secured and safe security measures comprising the Extended Access Control based access control, are able to direct to enhance economic advantages. In addition, a lot of novel high-tech tools could be applied at a cost savings to present techniques. Here we are having more significant, potential safer methods for customers shopping, self-storage information preservation, premises access and all areas of life in the future (Ghaboosi, 2009). Transforming and integrating your biometrics capability New technology based biometric systems are further improving the simplicity of use, exactness and performance of customary biometrics technology based solutions. In fact, they are offering more dependable identity authentication in criminal justice and health care and facilitating in a great deal of efficient way regarding well-organized immigration and cross-border travel. In this scenario, they also integrate one more layer to police and defense security procedures. At the present, biometric systems are integrated in the following working and operational areas of organizations (Jain & Kumar, 2010; Groupe Steria SCA, 2012): Compliance: In this scenario, this technology is being used to sure that we have the technical potential to broadcast information in line with international and national technology requirements for transmitting and receiving data (Jain & Kumar, 2010; Groupe Steria SCA, 2012). National and international policies: In this scenario, this technology provides an excellent support for managing workflow and data gathering, recommendation on usage, capture as well as storage of biometric data and information about regulations leading human rights, confidentiality, data safety, etc. (Jain & Kumar, 2010; Groupe Steria SCA, 2012). Architecture: The research has shown that biometric technology offers support for the architectural design of our technology solutions (Jain & Kumar, 2010; Groupe Steria SCA, 2012). Integration: Biometrics technology and related integrating biometrics solutions with an organization’s existing internal or 3rd party solutions and managing a wide variety of interfaces with other organizations (Jain & Kumar, 2010; Groupe Steria SCA, 2012). Operations: Biometrics technology offers support for maintenance of various operations of an organization. As discussed above, it automates various processes of an organization (Jain & Kumar, 2010; Groupe Steria SCA, 2012). Issues in Integrating Biometrics Though in the past few years the applicability and feasibility of biometric systems and procedures to deal with serious security issues have been extensively assessed by a large number of researchers, the real service of biometric systems remained restricted to low level monitoring systems; in face only a few considerable efforts have been less than overwhelmingly flourishing. However yet, the discussion of huge-scale biometric system persists to develop, immediately as the biometric business itself. With the exception of the fundamental problems of cost-per-checkpoint and type one as well as type two capacity, many complicated and consistent issues are concerned with the variety and flourishing accomplishment of biometrics systems. Though these “integration problems and challenges” are can recapitulated in an extremely a small part of the concerns, the problems themselves simply be evaluated and tackled with respect to the explicit, comprehensive needs of a specific application (Dechman, 2013). In addition, biometric systems integration issues have come up since, normally, we can see no totally acceptable and reasonable explanation that a biometric system will be suitable for every area of a definite system. It is an admitted fact that the integration of biometrics into a complete system solution necessitates effective implementation of application systems and interfaces, as well as the incorporation of both custom and commercial processes into an exclusive application, that is custom customized to convince all the functional, operational practical needs of the system. Additionally, it is essential for an organization to carry out a systematic evaluation of the biometric system and products before it decides for the integration of a biometric technology based system has been chosen for a specific application. In addition, it should also keep in mind the comprehensive requirements of the system. This procedure will recognize the needs for custom system and structure development and integration. The integration of biometric technology issues, then, is an evaluation of how to carry-out the development and incorporation of custom abilities as well as to put together the application through the obtainable system arrangement (Dechman, 2013). Integrated Biometric Technology Development This section outlines some of the important aspects that need to be considered while integrating biometric system; these aspects are further divided into different categories (Dechman, 2013): Device Capabilities Application programming interface or simply API Computer system interfacing functionalities and support Existing or built-in tools Issues in Development Given below are some of the important aspects regarding the development of biometric technology. These aspects need to be carefully considered for the successful integration of biometric systems (Dechman, 2013): Development of biometric technology systems and tools Source code accessibility of the biometric technology Technical OEM/Integrator-Level Documentation Support Issues Given below are some of the important issues regarding support of the integration of biometric technology (Dechman, 2013): Manufacturer technical support for biometric technology development and application Manufacturer maintenance support for biometric technology development and application Third party support for biometric technology development and application Conclusions In the past few years, we have seen a large number of developments and advancements in every walk of life. In this scenario, information technology has played a significant role in modernization of this world. In addition, there is also increase in criminal acts. In fact, the majority of criminal acts are now organized by making use of latest information technology tools and techniques. In addition, different private and government organizations use a wide variety of security measures to ensure security of their resources (human as well as other resources). Existing techniques to ensure security and human identification which are based on deriving credentials through PIN and identification credentials are not capable to deal with the increasing requirements for strict security measures in implementation of border crossings, national ID cards, access control and government profits. In this scenario, biometric recognition or simply biometrics systems, which are based on human identification through their behavioral and physiological features, are being more and more implemented and mapped to quickly growing human recognition tools and applications. Additionally, a biometric system authenticates people on the basis of their physical characteristics such as their eyes, fingers, hands and so on. There are many kinds of biometrics and they all follow the same principles. However, the basic purpose of all kinds of biometric systems is to ensure maximum security. The research has shown that biometric systems provide generally secure and suitable authentication mechanism. One of the most important advantages of biometric systems is that these systems identify a person on the basis of their characteristics which cannot be stolen, borrowed or elapsed and falsify hence these systems are believed to be more realistic and useful. This paper has discussed some of the important aspects of biometric systems. This research basically focused on the integration of biometric systems with other systems. This paper has also discussed the possible issues that can arise as a result of this integration. This paper has also discussed various solutions to these issues. This paper has also presented recommendations that need to be kept in mind while integrating biometric systems. References Al-Hijaili, S. J., & AbdulAziz, M. (2011). Biometrics in Health Care Security System, Iris-Face Fusion System. International Journal of Academic Research, Volume 3 Issue 1, pp.11. Dechman, G. H. (2013). Issues in Integrating Biometrics. Retrieved February 17, 2013, from http://www.fpusa.com/docs/IssuesinIntegratingBiometrics.pdf Gokulkumari, G., & Lakshmi, A. (2011). Study of Effects and Perceptual Analysis in Implementing Biometric Authentication. European Journal of Scientific Research, Volume 61 Issue 2, pp. 242-254. Groupe Steria SCA. (2012). New-generation biometrics for today’s security and identity challenges. Retrieved February 13, 2013, from http://www.steria.com/fileadmin/com/sharingOurViews/publications/files/Brochure-Biometrics.pdf.pdf Han, Y. (2011). 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