Extract from: D. Maltoni, D. Maio, A.K. Jain, S. Prabhakar Handbook of Fingerprint Recognition Springer, New York, 2003 Chapter 1: Introduction (Copyright 2003, Springer Verlag. All rights Reserved.) 1 Introduction 1.1 Introduction More than a century has passed since Alphonse Bertillon first conceived and then industri- ously practiced the idea of using body measurements for solving crimes (Rhodes, 1956). Just as his idea was gaining popularity, it faded into relative obscurity by a far more significant and practical discovery of the distinctiveness of the human fingerprints. In 1893, the Home Minis- try Office, UK, accepted that no two individuals have the same fingerprints. Soon after this discovery, many major law enforcement departments embraced the idea of first “booking” the fingerprints of criminals, so that their records are readily available and later using leftover fingerprint smudges (latents), they could determine the identity of criminals. These agencies sponsored a rigorous study of fingerprints, developed scientific methods for visual matching of fingerprints and strong programs/cultures for training fingerprint experts, and applied the art of fingerprint recognition for nailing down the perpetrators (Scott (1951) and Lee and Gaensslen (2001). Despite the ingenious methods improvised to increase the efficiency of the manual ap- proach to fingerprint indexing and search, the ever growing demands on manual fingerprint recognition quickly became overwhelming. The manual method of fingerprint indexing re- sulted in a highly skewed distribution of fingerprints into bins (types): most fingerprints fell into a few bins and this did not improve search efficiency. Fingerprint training procedures were time-intensive and slow. Furthermore, demands imposed by the painstaking attention needed to visually match the fingerprints of varied qualities, tedium of the monotonous nature of the work, and increasing workloads due to a higher demand on fingerprint recognition ser- vices, all prompted the law enforcement agencies to initiate research into acquiring finger- prints through electronic media and automate fingerprint recognition based on the digital representation of fingerprints. These efforts led to development of Automatic Fingerprint Identification Systems (AFIS) over the past few decades. Law enforcement agencies were the earliest adopters of the fingerprint recognition technology, more recently, however, increasing 1 Introduction 2identity fraud has created a growing need for biometric technology for person recognition in a number of non-forensic applications. Biometric recognition refers to the use of distinctive physiological (e.g., fingerprints, face, retina, iris) and behavioral (e.g., gait, signature) characteristics, called biometric identifiers (or simply biometrics) for automatically recognizing individuals. Perhaps all biometric identifiers are a combination of physiological and behavioral characteristics and they should not be ex- clusively classified into either physiological or behavioral characteristics. For example, fin- gerprints may be physiological in nature but the usage of the input device (e.g., how a user presents a finger to the fingerprint scanner) depends on the persons behavior. Thus, the input to the recognition engine is a combination of physiological and behavioral characteristics. Similarly, speech is partly determined by the biological structure that produces speech in an individual and partly by the way a person speaks. Often, a similarity can be noticed among parent, children, and siblings in their voice, gait, and even signature. The same argument ap- plies to the face: faces of identical twins may be extremely similar at birth but during devel- opment, the faces change based on the persons behavior (e.g., lifestyle differences leading to a difference in bodyweight, etc.). Is this person authorized to enter this facility? Is this individual entitled to access privi- leged information? Is the given service being administered exclusively to the enrolled users? Answers to questions such as these are valuable to business and government organizations. Because biometric identifiers cannot be easily misplaced, forged, or shared, they are consid- ered more reliable for person recognition than traditional token- or knowledge-based methods. The objectives of biometric recognition are user convenience (e.g., money withdrawal without ATM card or PIN), better security (e.g., difficult to forge access), and higher efficiency (e.g., lower overhead for computer password maintenance). The tremendous success of fingerprint- based recognition technology in law enforcement applications, decreasing cost of fingerprint sensing devices, increasing availability of inexpensive computing power, and growing identity fraud/theft have all ushered in an era of fingerprint-based person recognition applications in commercial, civilian, and financial do