FINGERPRINT BIOMETRICS

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1 FINGERPRINT BIOMETRICS White Paper JAN KREMER CONSULTING SERVICES Fingerprint Technology White Paper Page 1

2 TABLE OF CONTENTS 1. INTRODUCTION DOCUMENT OUTLINE BIOMETRICS OVERVIEW BIOMETRIC TECHNOLOGY TYPES FINGERPRINT TECHNOLOGY INTRODUCTION TEMPLATE AND MINUTIAE TYPES Minutiae Pattern based template Technical Comparison ON CARD MATCHING VERSUS OFF CARD MATCHING Differences of security levels with Biometric matching systems Match in PC / store on smart card (off-card) Match on card / Store on card Fingerprint Technology White Paper Page 2

3 1. INTRODUCTION This white paper provides an overview of Fingerprint Matching technologies For an overview of the complete overview see my White Paper Biometrics 1.1. Document Outline Chapter 1 provides an introduction and outline of this document with a general biometrics introduction Chapter 2 provides an overview of the different fingerprint matching technologies as well an overview of the template/minutiae types and standards Chapter 3 provides an overview off card versus on card matching Fingerprint Technology White Paper Page 3

4 1.2. Biometrics Overview Biometric technologies are automated methods for recognizing individuals based on biological and behavioral characteristics. Biometric technology involves the capture and storage of a distinctive, measurable characteristic, feature, or trait of an individual for subsequently recognizing that individual by automated means. A biometric system is essentially a pattern recognition system that recognizes a person by comparing the binary code of a uniquely specific biological or physical characteristic to the binary code of the stored characteristic. Samples are taken from individuals to see if there is a similarity to biometric references previously taken from known individuals. The system then applies a specialized mathematical algorithm to the sample and converts it into a binary code and then compares it to the template sample to determine if the individual can be recognized. In the case of access control, a person re- questing access will be asked to submit a sample and (of- ten, but not always) claim an identity or oneness of source with a template already stored. If the acquired sample is adequately similar to the claimed stored tem- plate, the access authorizations for the template can be checked and applied to the live person now seeking access. A reference model or reference containing the biometric properties of a person is stored in the system (generally after data compression) by recording his/her characteristics. These characteristics may be acquired several times during enrollment in order to get a reference profile that corresponds most with reality. Fingerprint Technology White Paper Page 4

5 Universality Distinctiven ess Permanence Collectabilit y Performanc e Acceptabilit y Circumvent ion Jan Kremer Consulting Services (JKCS) Biometric characteristic Facial H H L H M H L Hand vein M M M M M M L Gait M L L H L H M Keystroke L L L M L M M Odor H H H L L M L Ear M M H M M H M Hand M M M H M M M Fingerprint M H H M H M M Face H L M H L H H Retina H H M L H L L Iris H H H M H L L Palmprint M H H M H M M Voice M L L M L H H Signature L L L H L H H DNA H H H L H L L Biometrics refers to an automatic recognition of a person based on her behavioral and/or physiological characteristics. Many business applications (e.g. banking) will in future rely on biometrics since using biometrics is the only way to guarantee the presence of the owner when a transaction is made. For instance, fingerprint-based systems have been proven to be very effective in protecting information and resources in a large area of applications. Fingerprint Technology White Paper Page 5

6 This measurable characteristic, the biometric, can be primarily anatomical such as eye, face, finger image, hand, and voice or primarily behavioral such as sig- nature and typing rhythm, but most biometrics combines both anatomical and behavioral components. The biometric system must be able to identify a person based on one or a combination of these biometric identifiers quickly, automatically, and with little or no human intervention in the decision. With biometric technology, a more robust level of security and protection can be achieved in the identification component of access control, ID, and verification programs. Three basic means or levels of identification are often referred to in identity management functions: The lowest level is defined as something you have in your possession, such as an ID badge with a photograph on it. The second level is something you know, such as a password used with computer login or PIN code to use at a bank ATM. The highest level is who you are, which encompasses biometrics; the measurement of physical characteristics or traits. It is important to note that biometric technologies, even at their best, are not the panacea to security and identification issues. To achieve the most robust level of security biometric technologies need to be part of a broader and complete risk management system that incorporates multiple security technologies. Fingerprint Technology White Paper Page 6

7 1.3. Biometric Technology Types Jan Kremer Consulting Services (JKCS) When used for personal identification, biometric technologies measure and analyze human biological and behavioral characteristics. Identifying a person s bio- logical characteristics is based on direct measurement of a part of the body fingerprints, hand structure, facial features, iris patterns, and others. The corresponding biometric technologies are fingerprint recognition, hand geometry, facial, and iris recognition, among others. Biometric systems using predominantly behavioral characteristics are based on data derived from actions, such as speech and signature, for which the corresponding biometrics are speaker verification and dynamic sig- nature analysis. Almost all biometrics incorporate both biological and behavioral components. Biometrics is a very effective personal identifier because the characteristics measured are distinct to each person. Unlike other identification methods that use something a person has, such as an identification card to gain access to a building, or something a person knows, like a password or PIN to log on to a computer system, the biometric characteristics are integral to something a person is. Because they are tightly bound to an individual, biometrics is more reliable, cannot be forgotten, and is less likely to be lost, stolen, or otherwise compromised. Fingerprint Technology White Paper Page 7

8 2. Fingerprint Technology Jan Kremer Consulting Services (JKCS) 2.1. Introduction Some would argue that fingerprint identification was not a true biometric until the emergence of the more recent fully automated systems. More accurately, fingerprints represent the transition from a manual biometric to the automated form of the technology. Fingerprints have long been used to identify people. In the 14th century China, they were used as a form of signature. Today, it can be said that fingerprint verification technology is the most prominent biometric technology, used by millions of people worldwide. It is estimated that the number of possible fingerprint patterns is 10 to the 48th power.24 Fingerprint technology can be used effectively in both verification (1:1) and identification (1:N) applications. Fingerprint verification systems work by identifying the locations of small lines or ridges found in the fingerprint. They extract features from impressions that are made by these distinct ridges. Typically, fingerprints are either flat (capture by placing a finger directly on the scanner) or rolled (rolling the finger from one edge of the fingernail to the other). A flat fingerprint is an impression of the area between the fingertip and the first knuckle; a rolled fingerprint also includes an impression of the ridges on both sides of the finger. Fingerprint-based systems can also be further categorized into four broad groups: Minutiae-based matching (analyzing the local structure), direct correlation Fingerprint Technology White Paper Page 8

9 techniques, optical comparison, and spectral ridge-pattern matching (analyzing the ridge or global structure) of the fingerprint. Most fingerprint technology vendors algorithms analyze minutiae points. The current international standard for minutiae extraction recognizes two common characteristics as comprising minutia points: ridge endings (the end of a ridge), and bifurcations (Y- shaped split of one ridge into two ridges). When fingerprint patterns are captured and analyzed, about 5% of all fingerprint patterns are arches; 30% are whorls; and 65% are loops, 26 divided approximately equally into left and right loops. Ridge Spectral Pattern-based Algorithms In matching ridge patterns, the image is divided into small square areas about 5 pixels on a side. The ridge wavelength, direction, and phase displacement for each small square is encoded and used as the basis for the biometric template. Ridge pattern matching algorithms use a process of aligning and overlaying segments of fingerprint images to determine similarity. Minutia-based Algorithms A typical fingerprint image may produce between 15 and 70 minutiae, depending on the portion of the image captured. The most prevalent minutiae are ridge endings.27 Minutiae algorithms plot the relative position and type of points (minutiae) where ridge lines branch apart (bifurcate) or terminate (end). Variations There are a number of variations to fingerprint matching algorithms and template formats, including optical techniques (dating to the 1960s and formerly of great interest to the FBI) and direct correlation techniques in which areas of ridge patterns from fingerprints are directly overlaid. Some fingerprinting sensors can detect when a live finger is presented, but cannot tell whether or not the fingerprint on the finger is live or synthetic. Single- finger flats are typically used for verification systems and/or in small to medium-sized identification systems. Accuracy and reliability are good for most Fingerprint Technology White Paper Page 9

10 applications. Several studies have reasonably shown, however, that identification accuracy increases substantially as the number of fingers (and thus fingerprints) used increases, indicating that at least four fingers should be used for larger-scale identification systems. Because of this, the use of multi- finger slaps can offer improvements in performance accuracy and efficiency over the use of single finger flats, especially since four fingerprints can be collected in each image. Slap fingerprints (slaps) are taken by simultaneously pressing the four fingers of one hand onto a scanner or fingerprint card. Slaps are also known as four finger simultaneous plain impressions. They are, simply, multiple flat fingerprints captured at the same time. Slap fingerprints have received increasing attention for possible use in large-scale fingerprint identification systems as a possible compromise between the use of rolled fingerprints and single- finger flat fingerprints. A number of issues must be addressed in order to use slap fingerprints in an operational system. It is critically important to enroll each of the prints in the correct finger order. Enrolling fingerprints out of sequence can result in increased user errors and false rejections. Operationally, slap fingerprint scanners tend to be larger and more expensive than single finger fingerprint scanners. Robustness Fingerprint patterns are stable throughout one s life- time, unique, and easily analyzed and compared. Fingerprint systems are easy to use, in most cases requiring the user to simply touch a platen with his/her forefinger In addition to being secure, most fingerprint systems are relatively inexpensive. Limitations Capable of high accuracy levels, fingerprint devices can suffer from usage errors when users are not properly trained in system usage and/or motivated to cooperate when placing their finger (s) on the reader. This is, of course, not limited to fingerprint systems and extends to all biometric technologies. Conditions must be right for accurate authentication; for example, wet or moist fingers, cuts on fingers, or dirt or grease can some- times affect the authentication process. Additionally, as with other biometric methods where a platen must be touched, some people are uncomfortable with touching something that other people have touched repeatedly before them; the resemblance to the use of doorknobs notwithstanding. Other concerns involve the aspects of occupational impact. The use of hands in constant contact with abrasives or chemicals may interfere with fingerprint readers. There are consistent reports of genetic influence in population segments regarding an Fingerprint Technology White Paper Page 10

11 impact on image quality, but good documentation on this outlier influence is hard to find. Applications Fingerprint biometrics has four main application areas: large-scale Automated Fingerprint Imaging Systems (AFIS) (that are generally used for law enforcement), fraud prevention in entitlement programs, physical access control (doors), and logical access to computer systems. Workstation access applications seem to be based almost exclusively around fingerprints due to the relatively low cost, small size (easily integrated into keyboards, mice, and laptops) and ease of integration Fingerprint Technology White Paper Page 11

12 2.2. Template and Minutiae Types Minutiae In biometrics and forensic science, minutiae are major features of a fingerprint, using which comparisons of one print with another can be made. Minutiae include: Ridge ending the abrupt end of a ridge. Looks like this(-) Ridge bifurcation a single ridge that divides into two ridges Short ridge, or independent ridge a ridge that commences, travels a short distance and then ends Island a single small ridge inside a short ridge or ridge ending that is not connected to all other ridges Ridge enclosure a single ridge that bifurcates and reunites shortly afterward to continue as a single ridge Spur a bifurcation with a short ridge branching off a longer ridge Crossover or bridge a short ridge that runs between two parallel ridges Delta a Y-shaped ridge meeting Core a U-turn in the ridge pattern The major Minutia features of fingerprint ridges are: ridge ending, bifurcation, and short ridge (or dot). The ridge ending is the point at which a ridge terminates. Bifurcations are points at which a single ridge splits into two ridges. Short ridges (or dots) are ridges which are significantly shorter than the average ridge length on the fingerprint. Minutiae and patterns are very important in the analysis of fingerprints since no two fingers have been shown to be identical. Fingerprint Technology White Paper Page 12

13 Ridge ending. Bifurcation. Short Ridge (Dot). Minutia-based Templates As in a pattern-based system, a capture device is used to take a graphical image of a fingerprint (live scan). Special software then analyzes the fingerprint image and determines if the image actually contains a fingerprint, determines the location of the core, the pattern type (e.g. right loop, left arch, etc.), estimates the quality of the ridge lines, and finally extracts minutia. Minutia, from a simple perspective, indicates where a significant change in the fingerprint occurs. These changes are shown below Overview of the usage of the Minutiae in an AFIS (Automated Fingerprint Identification System) Fingerprint Technology White Paper Page 13

14 Understanding that dark lines in the image represent ridges and light lines represent valleys, Arrow A shows a region where one ridge splits into two ridges (called a bifurcation) and Arrow B shows where a ridge ends. After locating these features in the fingerprint, the minutia extraction software determines a significant direction of the change (using Arrow B as an example, the significant direction starts at the end of the ridge and moves downward. The resultant minutia, in their simplest form are then the collection of all reasonable bifurcations and ridge endings, their location and their significant direction. Minutia are also assigned a measure of their strength. A set of minutia is shown in below. Fingerprint Technology White Paper Page 14

15 2.2.2 Pattern based template A capture device is used to take a graphical image of a fingerprint, typically captured as a TIFF (Tagged Image File Format) image. The graphical image obtained from the capture device is commonly referred to as a live scan to distinguish it from a template or print stored in a database. Processing software examines the fingerprint image and locates the image center, which may be off-center from the fingerprint core. The image is then cropped a fixed distance around this graphical center. The rectangle in figure below details this cropped region. The cropped region is then compressed and stored for subsequent match. Fingerprint matching with pattern-based templates involves making a graphical comparison of the two templates and determining a measure of the difference. The greater the difference the less likely the prints match Fingerprint Technology White Paper Page 15

16 2.2.3 Technical Comparison Template Size vs. Search and Match Speed On average, minutia-based templates are significantly smaller than pattern-based templates on a byte count basis. The size of a minutia template is directly related to the number of minutia extracted. Minutia templates typically average about 350 bytes, or approximately 35 minutias, but can be as small as 125 bytes. The minutia extraction software is easily able to affect the size of the template by controlling the number of final minutia based on their strength. Pattern-based templates average about bytes when compressed, and about 1024 bytes when uncompressed. Matching and other related functions can only operate on the uncompressed version. However, the size of the template is directly related to the image and cannot easily be controlled without sacrificing detail (and thus usefulness) in the image. Template size and storage capacity are directly related, with minutia templates requiring about half the storage of pattern template. This impacts storage media costs, network bandwidths, etc., and has a direct effect on the time required retrieving a template for searching and matching. Template size also directly relates to the search and match speeds. Although search and match speeds are also dependant on the efficiencies of the algorithms involved, smaller templates will usually result in shorter match time. Sensitivity to Physical Changes Physical changes to the finger include such things as scars, cuts, folds, various blemishes, etc. Physical changes can occur through accident or as a normal course of work, such as cement workers, bricklayers, etc., whose fingerprint ridges are usually severely worn. When a minutia based system processes a fingerprint, a scar, fold or other blemish may result in a few minutia, but these typically represents a small percentage of the total minutia extracted. For example, if 20% of the extracted minutia is disrupted due to physiological changes to the fingerprint since the template was first taken, then there is still 80% of the minutia available for matching. Since a good match can be made with as few as 30% of the minutia, 80% availability provides for a wide safety margin. Fingerprint Technology White Paper Page 16

17 Minutia templates are therefore very forgiving of physical changes to the fingerprint without having to resort to re-extracting a new template from a new image of the finger. On the other hand, pattern-based templates are more sensitive to physical changes in the fingerprint since the matching is done based on the cropped fingerprint image. Physical changes can obscure critical elements of the image and significantly increase differences between two images of the same finger, thus reducing the likelihood of obtaining an accurate match. In a pattern-based system, new scars or other blemishes typically require a new image of the fingerprint be obtained, converted to a template and stored in the system. This presupposes the person is readily available for this activity, which may not be the case if, for example, the original print was a latent print taken from a crime scene. Template Efficacy In practical applications, such as the FBI s Integrated Automated Fingerprint Identification System (IAFIS), prints obtained by the FBI (from a ten print, for example) are matched against subsequent prints which may be taken from crime scenes, etc. Physical characteristics of a fingerprint, such as rotational orientation, completeness, ridge quality, etc., can vary greatly from crime scene to crime scene, and as contrasted to the AFIS database. Consequently, for matching algorithms and their respective templates to work well in this real-world environment, they must be able tolerate oftentimes complex variations in the prints. As previously discussed, pattern-based template matching is more sensitive to variations in the physiological characteristics of the fingerprint, which includes characteristics such as completeness, rotational orientation, etc. Understanding that scars and other blemishes can significantly hamper matching by obscuring relevant portions of the image, it is easy to see how a partial print, say from an edge of the print, can have the same adverse effect. In the case of minutia templates, there can be great variations in the environmental and physiological effects on the print since only a relatively small percentage of the minutia have to match for two templates to be adjudged identical. If, for example, 12 minutias matching out of 40 constitute a successful match, then any 12 of the 40 will typically do. If the minutia extracted from a sample print is very poor on the left side of the print then matching minutia can come from the right side, etc. Consequently, minutia-based templates are more robust in practical applications than are pattern templates. Fingerprint Technology White Paper Page 17

18 Security and Playback Security is a major consideration when discussing template types. A possible technique to circumvent fingerprint biometric security would be to obtain an actual template and replay it to the authentication system. We note that even with a variety of security and encryption methods, templates must still be decrypted, etc., in order to be used by the match algorithms. Consequently, a determined individual can most likely obtain a template. When a minutia-based template is extracted from a fingerprint, subtle variations in the orientation and centering of the finger on the capture device have subtle affects on the minutiae generated. This has no effect whatsoever on the accuracy of the matching algorithms (as previously discussed, minor variations in the minutia do not affect the match outcome). Consequently, the same finger presented multiple times will match, but not perfectly in the sense that the extracted templates will never be absolutely identical. This directly leads to a method for detecting the presentation of a stolen template: if the match is exact, the template must be an identical match, minutia for minutia, with the template in the database. The template must therefore be a duplicate of the one in the database and could not have come from a live scan. With a pattern-based template, obtaining the template, since it is a cropped graphical image of the fingerprint, gives you the actual fingerprint. Adding logic in the authentication system to detect that the exact same fingerprint image is being presented increases the False Reject Rate, that is, the number of valid users being rejected. Further, the stolen print from the template can be subtly altered so as to prevent a duplication detection, but still result in a positive match. With a minutia template, the fingerprint cannot be reconstructed, and thus the fingerprint itself cannot be subtly altered and then replayed to the authentication system. Performance Recent independent test results 1 have demonstrated exceptionally strong performance of the ISO based minutia algorithm with respect to the False Rejection Rate (FRR) measurement over an extended period of time after initial enrollment. This important metric measures the likelihood of an authorized user being denied access as time passes beyond the initial enrollment and hence is very relevant to real-world performance Fingerprint Technology White Paper Page 18

19 3. On Card Matching versus Off Card Matching This section provides information as to why on-card matching provides the most secure biometrics matching functionality as required for secure transactions for the current scope of the project and also guarantees a sophisticated secure biometric application system for future requirements. On-card matching is required to ensure that secure transactions such as PIN UNLOCK are provided in a secure environment. This could also include in the near future secure identification of card holders in a high security environment such as access to secure offices/buildings Differences of security levels with Biometric matching systems From a security aspect, the two important parts of a biometric system are Storing (on a server, in the PC, in the capturing device, in a smart card) Matching (on a server, in the PC, in the capturing device, in a smart card) Depending on how these parts are combined, the security implications of the system are different Match in PC / store on smart card (off-card) Storing the template in a smart card but match in the PC eliminates some of the problems with variant. The template is not available for hacking since it is securely stored on the smart card. However, the critical information i.e. the template is sent to the PC from the card when matching. This means that there is a risk the secret template can potentially be tampered with or stolen. Conclusion: The user can carry his own template (stored in the smart card) The user might use the fingerprint/smart card for accessing multiple devices The templates are exposed during verification The solution cannot be used for secure network transactions Fingerprint Technology White Paper Page 19

20 3.1.3 Match on card / Store on card Jan Kremer Consulting Services (JKCS) Both matching and storing on the card mean that the sensitive data i.e. the secret template never leaves the card. There is also no secret to steal since a successful match enables the use of certificates on the card without the need to use a PIN or password.. This method is normally seen as the most secure way of biometrically securing computers, networks and digital information in general. Conclusion: The smart card is made personal; it cannot be accessed without the appropriate biometric authentication. The templates are never exposed to a non-tamper proof environment. The user carries his or her own templates. The solution works with PKI (digital signatures, authentication over networks, encryption) without the need for a new infrastructure. Fingerprint Technology White Paper Page 20

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