Biometrics Technology and the New Economy: A Review of the Field and the Case of the United Arab Emirates

Over the past decade, biometrics technology has evolved from a technology used primarily in forensics and a narrow scientific and technological field to an indispensable technology in public and private sectors expanding into areas calling for advanced security. Biometric technologies provide high levels of security and reliability to address requirements related to identification and verification of personal identities. In light of the ever increasing requirements for robust identity management, biometrics industry is evolving to play a central role in shaping the future economy. This article provides a comprehensive overview of biometrics technologies, its functions, areas of application, related international standards, and recent advances in the field. The second part of the article looks at the application of biometrics in the government sector worldwide, and the emerging pivotal role of biometrics in consolidating the foundations of the digital economies and sheds light on the experiences of the United Arab Emirates in deploying different advanced biometrics technologies in a wide range of applications. It also outlines the government plans to develop an identity management infrastructure to address multiple strategic objectives, some of which are related to revolutionizing public services and supporting the development of the digital economy. DOI: 10.4018/jide.2012100101 2 International Journal of Innovation in the Digital Economy, 3(4), 1-28, October-December 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. necessitated varying methods of recognition and unique identification. Derived from the Greek words: Bios (Life) and Metron (Measurement), biometrics represents the science of identity recognition. Biometrics as a science and an automated means of identification may only be a few decades old, but as a concept, it has been in existence for thousands of years (Figure 1 and Table 1). Today, biometrics identification is recognized worldwide as a definitive personal identification method with specific metrics that gives both the service provider and the end user the assurance of a rapid, secure, and convenient transaction. Clearly, personal identification has become a key requirement for today’s increasingly digitised global economy. Indeed, trust in electronic transactions is essential to the vigorous growth of the global economy. Although markets shrink and expand in cyclical fashion, emerging nations continue to present emerging new markets with endless opportunities. However, globalisation is generally raising the level and intensity of competition to deliver better, faster, cheaper services and products in a secure and trusted environment. Businesses find themselves in need for modern identification solutions ever than before to establish such trust basis, i.e., for denial and accordance, and for acceptance and refusal. Businesses and governments alike in the past decade have therefore paid high attention to protecting their infrastructures from impersonating and/or infiltrating activities; a crime that was reported to cost 35 billion dollars in the United States alone in 2011 (Vamosi et al., 2011). With such justified attention to identification requirements, methods of identification assumed greater prominence. In addition, as e-government and ecommerce initiatives proliferate, offering more online electronic services, robust identification and authentication methods are needed to address control and security requirements. The existing literature referred widely to the fact that one of the main challenging issues facing e-government and electronic society’s development is identity management and the issue of trust in online transactions and digital identities. Ultimately the digital identity needs to become the same as real-world human identity. Using biometric identifiers for identity management provide strong credentials and higher levels of identity assurance. According to a recent research report by RNCOS E-Services, the global biometric market is anticipated to grow at a CAGR of around 22% between 2011 and 2013 (RNCOS, 2011). At a regional level, North America was reported to dominate the global biometric market share of over 30% in 2010. The Asian, Middle East and Africa region were expected to emerge as growing markets for biometrics by 2013. Figure 1. Late B.C. Picture writing of a hand with ridge patterns was discovered in Nova Scotia International Journal of Innovation in the Digital Economy, 3(4), 1-28, October-December 2012 3 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. The report has also indicated that the government sector accounts for the major share of the biometrics market whereas the healthcare and financial sectors emerging as the potential adopters of biometrics systems. Many banks in developing countries (specifically Asian nations, including India, China, Malaysia, etc.) have adopted biometrics to address identity fraud issues and to offer customers an easy and more convenient authentication alternative to cards and PINs for transactions like ATM withdrawals. Once again, biometrics is seen to be a critical enabler for the new digital economy. Only by understanding its potentials, how it works, and building on the experiences gained from international implementations, we can expect to make significant progress in creating successful future for our societies. As such, this article is written in this scope of dialogue. This article is structured as follows. The first section provides a general overview of biometrics, including its characteristics, applications fields, related international standards, and recent advances that are shaping the biometrics industry. The second section looks at how biometrics technologies are adopted in the government sector, and its emerging role in addressing identity management requirements and forming the basis for the new digital economy. The third section then looks at some biometrics initiatives implemented in the United Arab Emirates over the past decade to address needs related to critical infrastructure systems development. Finally, the fourth section presents an overview of one of the recent multi-billion dollar programs implemented to develop an identity management infrastructure to act as a single source for personal identity provision in the country. 1.1. Biometric Characteristics Biometric characteristics are divided into two broad categories; physiological and behavioural. Physiological characteristics are the ones that are closely linked to the human body. Iris, retina, facial features, fingerprint, palm print and DNA are physiological characteristics of the human body; offering positive identification that is difficult to counterfeit. Voice, speech, signatures, handwriting, key stroke sequences are characteristics used for behavioural pattern studies. In order to recognize a person by his or her biometric characteristics and derived biometric features, an enrolment process must take place. The process entails the construction of a data record of the enrolled person and to store it in a biometric enrolment database. The enrollment data record may comprise one or multiple biometric references and arbitrary non-biometric data e.g., name, and personal information, etc. See also Figure 2. The recognition process is initiated when the person to be recognised presents his or her biometric characteristic to the biometric capture device. The device generates a recognition biometric sample with biometric features which are compared with one or multiple biometric templates from the biometric enrolment database. This should result in acceptance or rejection of the recognition request. The most common capturing process in biometrics today is optical. In most cases minTable 1. Recognition means in the history of civilisation Research Reference Evidence Renaghan (2005) Details of a cave dating 31,000 years back revealed hand prints of pre-historic humans with pre-historical pictures apparently signed by fingerprint stamps of authors. McMahon (2005) Chinese and Indian historians have references of fingerprints used as signatures in transactions going back five thousand years. International Institute of Hand Analysis (2005) The Babylonian clay tables of 500 BC show evidence that human kind used to record business transactions and sign it using fingerprint stamps. 4 International Journal of Innovation in the Digital Economy, 3(4), 1-28, October-December 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. iaturised CCD cameras are used, which capture either visible or infrared light (Brüderlin, 2001). Recent methods, particularly in fingerprint capturing, try to get away from the optical capture to use temperature, pressure and/or capacitance (ibid). The primary performance evaluation measures in biometric systems are depicted in Table 2. However, the accuracy of these measurements varies, which has a direct relevance on the levels of security they offer (Shoniregun & Crosier, 2008). The error rates of biometrics systems are tuneable, which allows it to be configured according to the business objectives. The error rates in biometrics cannot be entirely tuned down, however reducing one error rate will increase the other. A balance between risk (i.e., false accept) and operability (i.e., false reject) must be found which matches the business objectives best. As depicted in Figure Figure 2. Enrolment and verification processes Table 2. Biometric performance measures Performance Measure Description False (non-match) rejection rate (FRR) or type I error The measure of the percentage of times a valid subject has been falsely rejected by the system. FRR (%) = number of false rejections * 100/total number of unique attempts. False (match) acceptance rate (FAR) or type II error The measure of the percentage of times an invalid subject has been falsely accepted by the system. FAR (%) = number of false acceptance * 100/total number of unique