Biometrics and E-Medicine: Marvel or Mayhem: Part 2

Case Studies

According to recent statistics cited by the First Consulting Group, a Long Beach, California consulting organization that specializes in health care consulting, only 3% to 5% of healthcare provider organizations have deployed biometrics [8].  Despite these statistics, several successful biometric pilots and full-scale implementations exist across the country. Consider Washington Hospital Center in Washington, D.C., a 975-bed not-for-profit hospital that implemented an iris-scanning system to increase security for their integrated medical record system – or Lourdes Hospital in Paducah Kentucky, which implemented NEC Technology’s HealthID finger-scan system in 1998 and currently stores 15,000 to 20,000 fingerprints in a patient and physician database [8]. In another example of a successful biometric implementation, Moffitt Cancer Center, a 160-bed research hospital in Tampa, Florida,  tested 60 biometric devices in early fall of 2002 with full rollout of 1,000 devices expected by June 2003 [11].  In the fall of 2000, the 660-bed Jackson-Madison County General Hospital implemented Identix fingerprint technology for  315 employees and affiliated physicians [9].  In April of 2002, the 281-bed Columbus Children’s Hospital in Ohio deployed a comprehensive program that requires more than 1000 doctors, nurses, and pharmacists accessing patient medical records and entering medicine orders by computer to authenticate via fingerprint scan [12]. North Florida Medical Centers in Tallahassee implemented biometric security solutions deployed to more than 100 users during a 6 to 8 month period [10].  Lastly, Children’s Hospital in Dallas plans to implement a single-sign-on application with iris scanning, fingerprint biometrics, or a combination of the two in 2003 [13].

In each of these case studies, biometrics were deployed with a specific implementation approach based on the appropriate solution methodology designed to meet each healthcare provider’s needs. With this approach, biometrics provide an effective means to address HIPAA mandates for secure access, storage, maintenance, and transmission of identifiable healthcare information between patients and hospital staff.  Combined with proper user training, IT support, and appropriate fallback measures, biometric technologies can successfully integrate with people and policy criteria.  And while many successful biometric deployments exist today, challenges lie ahead. From a technology perspective, biometric finger-scan devices remain susceptible to dust and dirt accumulation on the capture device itself.  Excessively dry or oily skin can also disrupt a finger-scan system and produce inaccurate readings. Voice authentication systems, though great for certain telecommunication applications, perform poorly in noisy environments. From a people perspective, inconsistent usage, poor training, or simple reluctance to use the biometric system can negatively impact a biometric deployment. From a policy perspective, no biometric system can guarantee 100% successful enrollments within the user population, dictating the need for secure, accurate, and reliable fallback procedures. For many healthcare organizations, the cost of meeting HIPAA requirements through the use of biometric applications remains a strong deterrent with some full-scale biometric implementations costing hundreds of thousands of dollars [11, 12]. Others argue that the cost of a biometric deployment pales in comparison to the legal fees incurred from attorneys hired to review privacy and security plans [14].


Do biometrics provide the ultimate cure for compliance with HIPAA security requirements?  Of course not – much the same way no specific technology resolves all the issues encountered in a complex enterprise security infrastructure with multiple work stations, user groups, software applications, and a litany of other variables to contend with.  Biometrics do, however, provide a robust, secure, and highly reliable means of user authentication. Biometrics also offer unprecedented logging and audit trail capabilities. When used in conjunction with single-sign-on applications, biometrics free healthcare providers from the hassles of the login, logout merry-go-round.  In the end, a poorly planned, arbitrary application of biometrics can do more harm than good, but a well-defined, well-designed application of this technology can provide a mature, scalable foundation from which to satisfy HIPAA security requirements.

Biometrics and E-Medecine: Marvel or Mayhem: Part 1

This paper explores the application of biometric technology as a viable approach to fulfill the security standards mandated by the Health Insurance Portability and Accountability Act (HIPAA). Through analysis of hospital and healthcare organization case studies, this paper examines the ability of biometrics to provide a safe, secure, and reliable means of user authentication via desktop and Internet applications. In addition, an unbiased look at the impact of biometrics with respect to people, policies, and existing security infrastructures provides valuable insight for healthcare industry leaders grappling with HIPAA security requirements.


Much has been published in recent months regarding the use of biometrics as a skeleton key solution designed to free the healthcare industry from the shackles of security compliance standards mandated by the Health Insurance Portability and Accountability Act (HIPAA). And while it’s true that biometrics provide a viable alternative to more traditional user authentication mechanisms like PINs, passwords, and magnetic swipe cards, HIPAA remains technology neutral, placing emphasis on when and why a security solution must be implemented rather than on how.  So why all the hype surrounding biometrics and their potential to satisfy HIPAA security requirements? The answer is complicated and remains at large without a better understanding of the various components that constitute an overall healthcare security infrastructure, a complex paradigm encompassing the confidentiality of patient records, as well as electronic access to patient information via multiple applications and platforms.

A secure, reliable, and inherently flexible healthcare security infrastructure contains the following four components described by the inner loop in Figure 1: authentication, authorization, digital signatures, and network security [1].  These four components stem from a public key infrastructure (PKI) designed to govern electronic transactions and provide a framework for securely delivering healthcare information across the Internet.

With hospitals and healthcare organizations required to provide patients with secure access to medical data over landline, wireless, and Internet applications, biometrics play a critical role in the user authentication space, addressing the question, “Are you who you claim to be?” And since a person’s biometric trait cannot be lost, stolen, or in most cases forged, biometrics provide stronger authentication security over passwords or token ID systems alone. In a sense, biometric authentication constitutes the first line of defense, followed by security authorization, which must determine whether or not a person has access privileges to a particular system. Digital signatures for Internet transactions handle non-repudiation, or the ability to guarantee that the authenticated individuals actually participated in the transaction. Network security provides the information assurance umbrella to protect the security system from unauthorized use as well as provide confidentiality of communication through encryption methods. Together, these four components of authentication, authorization, digital signatures, and network security form a sort of security nucleus with biometric authentication technology at the core and a PKI environment surrounding it.  In turn, biometrics within the PKI environment provide significant support for the five overarching HIPAA requirements.  The first of these requirements address electronic transactions, which dictate the need for standardized code sets for encoding data elements involved in the electronic transaction of healthcare claims, health care payment and remittance advice, benefit coordination, and other transactions. Privacy of individually identifiable health information establishes regulations that include consent, authorization notices, disclosure audits, and grievance procedures. Security rules define standards intended to protect confidentiality, integrity, and availability of healthcare information through technology neutral and technology scalable means. Administrative procedures dictate rules for access, whereas network security governs rules for logical network access and physical access controls for data rooms, equipment control, disaster recovery, and general facility access [15].

Technology, Policy, and People

With biometrics at the core, the deployment of biometric application software within a PKI environment can positively impact each of the five general HIPAA regulations by securing entire networks and all associated applications running across the healthcare continuum, including applications for computerized physician order entry systems, time and attendance logs, user audit trails, patient identification, data access, and more. But technology alone will not satisfy HIPAA compliance requirements, and healthcare organizations who embrace technology as a silver bullet solution to their HIPAA woes are in for a harsh ride when they realize 75% of HIPAA governs policies and procedures [7]. Unlike password guidelines, however, biometric policies dictate that you can’t share a finger or an eyeball when it’s time to authenticate on the system.  And in most cases, unless you’re prone to playing with live grenades or staring at the sun with a magnifying glass, you can’t lose your biometric attribute the way you lose a magnetic swipe card or personal identification number scribbled on the notepad in your drawer.  Unfortunately, too much attention is placed on the technology for technology sake and not enough on researching and establishing relevant security policies that define access privileges, fallback procedures, equipment maintenance schedules, and so on.

Along with security policy, people play a critical role in defining, implementing, and enforcing an effective biometric authentication solution. Regardless of the chosen technology and policy application, the appropriate personnel must define the security policy and ensure that users obey the rules and procedures described therein.  End users decide whether a particular technology suits their tastes or not.  Some end users find fingerprinting distasteful because of the negative connotation associated with law enforcement applications. Others find iris-scanning too invasive and perpetuate false concerns about potential damage to their eyes.   People, not technology or policy, make judgments about their personal comfort level with a given technology or system. If users are uncooperative, the biometric system can fail. If people neglect to follow directions when authenticating, the system will produce significant errors. If users don’t understand the importance of obtaining a quality enrollment image or the importance of consistent biometric presentation, the system will produce inconsistent results.  The technical and non-technical issues involving people are plentiful, and no authentication system, biometric or otherwise, will completely eliminate the need for some form of human intervention.

With an understanding of the roles that technology, policy, and people play in the overall establishment and execution of a security infrastructure with biometrics at the core, we are better equipped to answer the initial question regarding the use of biometrics to satisfy HIPAA security requirements.  In essence, biometrics provide a vehicle that can work equally well for physicians, nurses, administrative staff, and patients – all of whom must coexist in a dynamic healthcare environment shaped by technology, policies, and people.  Biometrics not only provide an effective means of user authentication, but also an effective means of integrating disparate information systems that communicate over wireline, wireless, and Internet paths both locally within a hospital setting and remotely at end user locations. The integration of biometrics with other technologies and the appropriate people and polices go a long way toward fulfilling HIPAA security requirements.  From a patient care perspective, biometrics allow multiple users to share a workstation while preserving the authentication and audit trails for each user [7].  In turn, physicians and nursing staff can focus more attention on patient care and less time on logging in and logging out of various applications. Furthermore, biometrics facilitate patient admission, speed access to prior medical records, and eliminate duplicate medical records [7].  In short, biometrics provide an effective means of managing access to patient records, preventing unauthorized use of system resources, and ensuring higher levels of information security.  It should be noted, however, that biometrics are not without fault and must be properly introduced to meet a particular healthcare provider’s needs, a facet of this technology often overlooked in a system integrator’s haste to deploy a quick-fix solution.


Lie Detection Systems: Part 2

1. Facial Thermography

Facial thermography represents a safe, non-invasive technology that measures skin surface temperature on a real time basis.  Like VSAs, facial thermography has an advantage over polygraph systems in that its non-invasive nature lends itself to covert applications.  Recently, Doctors B. M. Gratt and E. A. Sickles from the University of California at Los Angeles used microwave receivers to measure thermal radiation emitted from a human face and analyze blood flow differences between different regions of the face.  Last year, Doctors Levine, Palvidis, and Cooper refined the concept of facial thermography to explore the fact that specific activities are associated with characteristic, facial thermal signatures.

One 25-person pilot study conducted in 2002 between DoDPI and outside researchers examined the possible utility of a new thermal imaging device that measures the radiant energy emitted from an examinee’s face.  The published report, which focused on thermal imaging as an adjunct or potential alternative to traditional polygraph measurements, claimed that thermal imaging results achieved higher accuracy than the polygraph.  Although according to the 2003 National Research Council report on polygraph testing, the pilot study conducted by DoDPI failed to provide acceptable scientific evidence to support facial thermography as a viable method for detecting  deception.

1.1 Conclusions

Similar to other veracity studies done on emerging lie detection methods, studies based on facial thermography draw conclusions based on small sample sizes, uncontrolled environments, uncooperative subjects, inconsistent judging criteria, and other variables that detract from the scientific basis of which successful results are often cited.  Unless new research can provide acceptable scientific evidence to support facial thermography as a viable alternative to the polygraph, the concept of using thermal imaging as a method for lie detection will likely remain an adjunct to traditional polygraph measurements.

2. Functional Brain Imaging

Functional brain imaging looks at brain function more directly than polygraph testing by using positron emission tomography (PET) and magnetic resonance imaging (MRI), which employ strong magnetic fields to induce brain tissue molecules to emit distinctive radio signals used to monitor blood flow and oxygen consumption in the brain.  Within the context of MRI, the detection of blood-oxygen-level-dependent (BOLD) signals has garnered the name functional magnetic resonance imaging (fMRI).  Research studies are focused on using fMRI to analyze knowledge and emotion characteristics theorized to link deception to physiological brain activity.  In addition, other research areas have focused on combining PET and fMRI with simultaneous measurements of skin conductance response.  Scientists are quick to point out, however, that applied fMRI studies completed thus far have similar limitations to earlier polygraph studies.  Furthermore, they point out that fMRI analysis is expensive, time-consuming (2-3 hours per examine), and highly sensitive to subject motion during the brain scan.  To overcome some of these potential deficiencies for veracity applications, some researchers suggest the use of an electroencephalograph (EEG), which directly measures the electrical output of the brain rather than attempting to map brain activity from blood flow patterns.  One EEG study conducted by Jennifer Vendemia, a researcher from the University of South Carolina, suggests that predictable patterns of energy fluctuating brain activity occur when people lie.  The correlation between brain activities and lying are nothing new, but the fact that researchers continue to explore this path makes the possibility of brain imaging a potential candidate to supplement or eventually replace traditional polygraph techniques.

2.1 Conclusions

Psychology professor John Gabrieli predicts that within ten years research advances in neurotechnology could yield brain scanners in schools and airports.  One Iowa company called Brain Fingerprint Laboratories claims it has developed technology that can identify specific brain wave patterns that people emit when they are looking at or discussing something they have already seen.  Furthermore, psychiatrist Daniel Langleben from the University of Pennsylvania School of Medicine has found that increased activity in several brain regions is visible in an fMRI scan when people lie.  However, Doctor Langleben also contends that lying is a complex behavior and that it is likely to be linked to a large number of unknown brain sites.

Lie Detection Systems: Part 1

1. Introduction

Techniques for lie detection have existed for decades through the use of interviews, interrogations, and other means based on little scientific merit.  Today, modern lie detection techniques rely on measurable physiological responses, which serve as indicators of deception.  This White Paper explores the concept of veracity by summarizing the following physiological techniques used for either overt or covert lie detection scenarios:

  • Polygraph
  • Voice Stress Analyzers
  • Facial Thermography
  • Functional Brain Imaging

2. Polygraph

Considered one of the best know and widely utilized lie detection techniques in the U.S. and other countries like Israel, Japan, and Canada, the polygraph provides U.S. law enforcement and intelligence agencies with a tool that combines interrogation with physiological measurements obtained during the polygraph examine.  By recording a person’s respiration, heart rate, blood pressure, and electrical conductance at the surface of the skin, the polygraph examination applies to overt scenarios where a subject is asked a series of yes/no questions while wired sensors relay data about the person’s physiological attributes.  In addition to these traditional measurements of involuntary and somatic activity, other physiological events can be recorded non-invasively, including cardiac output, total peripheral resistance, skin temperature, and vascular perfusion in coetaneous tissue beds.

Trained polygraph practitioners emphasize that the polygraph instrument itself measures levels of deception indirectly, by measuring physiological responses that are believed to be stronger during acts of deception than at other times.  Collectively, these patterns of physiological responses to relevant questions asked by an investigator are recorded on an analog or digital chart and require human interpretation from the polygraph examiner.  Aside from interpretation of the polygraph chart, which can also be somewhat automated by computer algorithms, other factors that assist or inhibit the polygraph instrument’s ability to accurately perform a lie detection function include the potential for influence from drugs or alcohol, examiner’s expectations about the examinee’s truthfulness, and adverse physiological responses that have no direct correlation to the examinee’s intent to deceive.

Aside from its use as a diagnostic tool to test for deception where truth or deception decisions are made based on charts that are analyzed and scored, the polygraph has other practical applications such as:

  • Eliciting admissions from people who believe or are influenced to believe that the polygraph machine will accurately detect their attempts at deception.
  • Testing the level of cooperation with an investigative effort through suspicion or detection of countermeasures used by the examinee during polygraph testing.

Overall, polygraph examines are considered to be an effective tool for lie detection when combined with information from other sources used to judge truthfulness or deception (i.e., pretest interviews, comparison question testing, observation of examinee’s demeanor, etc.).

2.1 Commercially Available Polygraphs

Digital quality, commercially available polygraph systems consist of either a complete hardware/software integrated system on a laptop PC or a stand-alone data acquisition system that can be connected to an existing computer.  Some systems include scoring algorithm software and/or peripheral hardware used for motion sensing or to measure additional physiological parameters.  The following list describes a few of the commercially available polygraph products:

  • LX4000 – manufactured by Lafayette Instrument.  Records, stores, and analyzes physiological characteristics derived from respiration, galvanic skin response, and blood volume/pulse rate.
  • 4-6 Channel S/Box Package – from Axciton Systems, Inc. provides a customized polygraph system designed to accommodate 4, 5, or 6, channel physiological parameters.
  • Computerized Polygraph System (CPS) – manufactured by Stoelting Polygraphs.  Claims to be the only computerized polygraph system containing a scoring methodology based on verified criminal data from a major government law enforcement agency.

2.2 Conclusions

As the subject of hundreds of controlled, scientific studies regarding polygraph effectiveness, a final and concrete determination of the polygraph’s accurateness still hinges on research information contained in classified national security documents as well as proprietary information about computer scoring algorithms or other trade secrets that equipment vendors will not divulge.  The American Polygraph Institute cites a 70% accuracy rating among polygraph skeptics with a 90% accuracy rate among proponents.  A 2003 report conducted by the National Academy of Sciences, which examined 57 previous polygraph studies to quantify the accuracy of polygraph testing within the scope of personnel security screening concluded, “The inherent ambiguity of the physiological measures used in the polygraph suggests that further investments in improving polygraph technique and interpretation will bring only modest improvements in accuracy.”  This study also pointed out that polygraph countermeasures deployed by major security threats could seriously undercut the value of polygraph security screening.  Nonetheless, this same study concluded that the polygraph technique is the best tool currently available to detect deception and assess credibility.

3. Voice Stress Analyzers

Touted as a lower cost, less invasive lie detection method, commercially available voice stress analyzers (VSAs) have been in use since the early 1970’s through efforts between private industry and the U.S. Army.  Based on the presumption that liars experience more stress than truth-tellers, a VSA works by measuring microtremors associated with laryngeal muscles used during voiced excitation.  The microtremor are defined as inaudible vibrations that speed up uncontrollably in the human voice during an act of deception.  The level of microtremor maintains an inverse relationship to a person’s stress level where more stress denotes less tremor.  Slow microtremors occur at rates between     3-5 Hz while more rapid tremors can occur at 6-12 Hz.  Microtremors can be affected by numerous variables, including age, stress, drugs, alcohol, medical illness, brain disorders, and multiple sclerosis.  Major issues surrounding VSA validity and accuracy remain focused on how stress impacts the laryngeal muscles during normal speech production and whether VSA speech processing algorithms can effectively extract and quantify the existence of microtremor information.  Proponents of VSAs point out several benefits of using their equipment in lieu of more traditional polygraph techniques, namely:

  • Applicability to covert scenarios.
  • Less training time required to learn and operate.
  • No academic prerequisites for training.
  • 30-50% less time to administer the testing regiment.
  • Voice recordings can be processed as well as live speech.
  • Lower cost of ownership.

3.1 Commercially Available VSAs

Commercially available VSAs use some form of speech signal processing to extract excitation information related to microtremors.  The following VSAs provide a sample of these commercially available products:

  • Psychological Stress Evaluator (PSE) – patented in the 1970’s by Allan D. Bell and marketed through Dektor Counterintelligence and Security, Inc.
  • Truster – developed by an Israeli company named Makh-Shevet.
  • Computerized Voice Stress Analyzer (CVSA) – developed in the late 1980s by the National Institute for Truth Verification (NITV), which claims their system is in use by more than 500 law enforcement agencies.
  • Lantern – developed by Diogenes Group, Inc.
  • Vericator (formerly known as Truster Pro) – manufactured by Trustech Ltd. Integritek Systems, Inc.
  • VSA Mark 1000 – manufactured by CCS International, Inc. and marketed as a covert electronic lie detection system.

3.2 Conclusions

One technical report conducted in 1999 by ACS Defense, Inc. and the U.S. Air Force Research Laboratory in Rome, N.Y., concedes that information from previous studies of speech under stress combined with their own Air Force evaluations and experiments using commercial VSAs suggests that a speaker’s voice characteristics change when the speaker is under stress.  However, as stated previously in other studies, a variety of factors in addition to stress can reflect changes in the human speech production process, including the presence or absence of microtremors.  In its final conclusions, the Air Force study determined that the level and degree to which changes in muscle control associated with speech production impart more or less fluctuation in the speech signal cannot be conclusively determined.  In other words, focusing on the absence or presence of microtremors alone does not conclusively define the accuracy of VSAs.  Furthermore, the study recommends that several speech features may be needed to accurately capture the subtle differences in how speaker’s convey their stress in various speech scenarios.

Another study conducted in 2000 by the Department of Defense Polygraph Institute (DoDPI) and the U.S. Army Walter Reed Hospital, also concluded that the relationship between microtremors and a speaker’s deception might not be experimentally sound and that the use of microtremor analysis to detect deception is nothing better than chance.  In addition, a 2002 study, conducted by the DoDPI research division staff to investigate the NITV’s CVSA, provided no evidence to support the CVSA for its ability to identify stress-related changes in voice.  Lastly, a 2002 VSA literature review conducted by the National Research Council revealed that VSA accuracy rates from commercially available systems remain at or below chance probability levels.  Still, despite the doubt from many researchers and published reports citing a lack of scientific evidence to support industry claims, the commercially available CVSA system, which retails for about $10,000, claims an accuracy rate of 98%.

Biometrics Demystified: Part 4

1.   Biometric Standards Organizations

Although a lot has been written about the lack of standards and testing for biometric technologies, much has changed in recent years with a surging interest in defining interoperability requirements for biometric applications.  Recent standards efforts aimed at creating application programming interfaces (APIs) will allow for simple substitution of biometric technologies within a given network environment along with streamlined integration of biometric technologies across various software applications.

1.1 NIST-ITL and CBEFF Standard

A division of NIST, the ITL performs testing, testing methods, and proof-of-concept implementations in an effort to help end-users and the biometric industry accelerate the deployment of standards-based security solutions based in part on the Government’s Homeland Defense Initiative.  In conjunction with the Biometric Consortium, the NIST-ITL initiated the Common Biometric Exchange File Format (CBEFF) project to establish a universal biometric template, which allows different systems to access and exchange diverse types of biometric data in a standardized format.  To date, CBEFF has been finalized and exists as a file header format with fields that define common elements for exchange between biometric devices and systems.  CBEFF also provides forward compatibility for technology improvements. CBEFF does not, however, provide device or matching interoperability.  On January 1, 2001, the NIST published the CBEFF specification as NISTIR 6529.

1.2 BioAPI Consortium

First introduced in 1998, the BioAPI Consortium developed a widely accepted API for biometric technologies.  Derived from various biometric industry leaders as well as non-biometric companies like IBM, HP, and Compaq, the BioAPI Consortium works with biometric solution developers, software developers, and systems integrators to leverage existing standards and develop an OS-independent standard that can serve various biometric technologies.  Unlike the CBEFF, BioAPI does not define how the biometric device captures the data, but rather, how applications communicate with biometric devices and how the data is manipulated and stored.  Written in the C programming language, BioAPI defines the application programming interface and service provider interface that define capabilities such as enrollment, verification, identification, capture, process, match, and store.  The Consortium published Version 1 of the BioAPI Specification in March 2000.  BioAPI Version 1.1 of the Specification and Reference Implementation was released in March 2001.  Recently, the U.S. Army announced that future Army procurements of biometric devices will require BioAPI compliance.

1.3 BAPI

Unlike the consortium-based BioAPI, BAPI was developed and owned by I/O Software, a biometric middleware vendor.  I/O Software has licensed BAPI to Microsoft, who plans to incorporate biometric authentication as a core component of its future OS.  I/O Software has also licensed elements of BAPI to Intel for inclusion into Intel’s PC security platform.  At present, BAPI remains a competing element against BioAPI but looks to be more prevalent in the Windows/Intel market than in U.S. Government applications, which have established BioAPI as their API of choice.

1.4 INCITS Technical Committee M1

In November 2001, the Executive Board of INCITS established the Technical Committee M1 to ensure a high priority, focused, and comprehensive approach in the U.S. for the rapid development and approval of formal national and international generic biometric standards.  M1’s mission involves accelerating the deployment of significantly better standards-based security solutions for purposes such as homeland defense and other government and commercial applications based on biometric personal authentication. At present, the BioAPI Specification Version 1.1 has successfully completed INCITS fast track processing and attained approval for maintenance under Technical Committee M1 on February 13, 2002.  An augmented version of CBEFF is next on the list for fast-track processing in the near future.  In addition, the Technical Committee M1 is reviewing contributions of draft project proposals for the standardization of biometric templates while seeking to develop active liaisons with other INCITS Technical Committees such as B10 – Identification Cards and Related Devices, L3 – Coding of Audio, Picture, Multimedia, and Hypermedia Information, and T4 – Security Techniques.


Additional standards currently under development by Technical Committee M1 include:

  • Application Profile: Verification and Identification of Transportation Workers;
  • Application Profile: Personal Identification for Border Crossing;
  • Application Profile: Biometric Verification in Point-of-Sale Systems;
  • Finger Pattern-Based Interchange Format;
  • Finger Minutiae Format for Data Interchange;
  • Face Recognition Format for Data Interchange;
  • Finger Image Interchange Format;
  • Iris Image Format for Data Interchange.


The ANSI Accredited Standards Committee (ASC) X9 develops, establishes, publishes, maintains, and promotes standards for the financial services industry in order to facilitate delivery of financial products and services.  The development of X9.84 Biometric Information Management and Security stemmed from the need to maintain confidentiality with biometric data.  X9.84 ensures the integrity and authenticity of biometric data by defining requirements for integrating biometric information such as fingerprint, iris scan, or voice print in a financial services environment where customer identification and employee verification are of paramount importance.


2.   Industry Associations

2.1 Biometric Consortium

The Biometric Consortium was established in 1992 by the U.S. Department of Defense and aims to create standards which can be used to test biometric technologies for the benefit of all government agencies.  The goals of the Biometric Consortium include:


  • Promote the science and performance of biometrics;
  • Create standardized testing and establish the National Biometric Evaluation Laboratory;
  • Promote information exchange between government, industry, and academia;
  • Address the safety, performance, legal, and ethical issues of biometric technologies;
  • Advise agencies on the selection and application of biometric devices.


The Biometric Consortium sponsors two working groups: one concerning CBEFF and another co-sponsored by the NIST known as the Biometrics Interoperability, Performance, and Assurance Working Group.  This latter group seeks to broaden the utilization, acceptance, and information sharing of biometric technologies among users and private industry supporters.  This group also supports the advancement of technically efficient and compatible biometrics technology solutions on a national and international basis by addressing required issues and efforts beyond the scope of current and on-going developments already undertaken by other national or international organizations.

2.2 BioSEC Alliance

Founded in 1999 by BioNetrix, the BioSEC Alliance forms a multi-vendor initiative dedicated to promoting enterprise authentication solutions.  The BioSEC Alliance promotes a range of biometric and non-biometric authentication technologies to suit various organizations’ requirements.

2.3 International Biometric Industry Association (IBIA)

The IBIA is a nonprofit trade association founded in 1998 to advance, advocate, defend, and support the collective international interests of the biometric industry.  Though not directly involved in standards development, the IBIA’s group of biometric developers, vendors, and integrators has used its influence to alter several pieces of recent government legislation, including the Identity Theft and Deterrence Act and the Electronic Signatures in Global and National Commerce Act.

Biometrics Demystified: Part 1


Unlike traditional authentication methods that rely on something you know – like a password or passphrase, or something you have – like a smart card or token, biometric applications rely on something you are: a human being with robust and distinguishable physical traits.  Because a person’s unique trait (iris, retina, fingerprint, voice, etc.) cannot be lost or stolen, biometric applications, when used in conjunction with traditional user authentication mechanisms, provide higher levels of security over traditional authentication methods alone.  Biometrics Demystified describes the field of biometrics as it exists today with an overview of how a typical biometric system works and how various biometric technologies provide a viable alternative to more traditional user authentication methods.

1.      Biometric System Elements

1.1 Identification

Identification attempts to answer the question, “Who are you?”  The Integrated Automated Fingerprint Identification System (IAFIS) established and administered by the Federal Bureau of Investigation (FBI) provides a well-known example of a biometric identification system.  With IAFIS, the FBI maintains the largest collection of fingerprint records at over 40 million ten print records.

1.1.1 Positive versus Negative Identification

Positive identification systems attempt to match a user’s biometric template with a match template stored in a database of enrollment data.  In these systems, the user will claim an identity by providing a name or a PIN before submitting their biometric sample.  Positive identification prevents multiple users from claiming a single identity.  Biometric systems deployed for positive identification include hand geometry, finger scan, voice recognition, iris scan, retinal scan, and facial scan.  In contrast, negative identification systems ensure that a user’s biometric data is not present in a given database, thus preventing a single user from enrolling more than once.  In this scenario, no reliable non-biometric alternatives exist. Welfare centers offer one example where a user could benefit from enrolling more than once to gain multiple benefits under different names.  Only two biometric systems are currently deployed for negative identification, namely finger scan and retinal scan.

1.2 Verification

In contrast to identification, verification, or one-to-one matching, attempts to pair a user’s biometric sample against his or her enrollment data.  In this mode, the user first claims their identity by entering a password, user ID, voice command, or other form of identification before processing the biometric sample.  Verification begs the question, “Are you who you claim to be?” For the most part, any biometric authentication system provides a good example of a verification system where users must identify themselves to the system and then verify that identity through a given biometric sample.  In general, verification systems (one-to-one) are faster and more accurate than identification (one-to-many) systems and require less computational power.

1.3 Enrollment

By relying on a user’s physical characteristics, biometric authentication attempts to match a user’s unique physical trait against a newly captured biometric sample of that user’s trait. By definition, enrollment describes the process by which a user’s biometric sample is initially acquired, processed, and stored in the form of a biometric template.  Depending on the system, a user may be required to present their biometric sample several times to achieve a successful enrollment.  Aside from the template creation, a system administrator creates a username or password associated with the user upon enrollment.  Enrollment effort can vary between biometric systems.  Often more than two attempts are required for fingerprint and voice systems where obtaining a good quality enrollment image can be heavily dependent on user behavior and familiarity

1.4 Presentation

Though both processes are similar, a distinction is made between presentation and enrollment, where presentation describes the process by which a user returns to a biometric application they have previously enrolled in and provides a biometric sample to the acquisition device.  The presentation process can last as little as one second or more than a minute, depending on the specific biometric technology deployed.

1.5 Data Collection

Data collection begins with the measurement of a user’s biometric characteristic (fingerprint, iris image, voice print, etc.).  At this stage, an assumption is made that the user’s biometric characteristic remains distinctive and repeatable over time.  The presentation of the user’s biometric characteristic to the biometric sensor introduces a behavior aspect to the biometric process.  The output from the sensor, which relies on the input from the user, derives itself from three factors:


  1. The biometric measurement;
  2. The way the measurement is presented by the user;
  3. The technical characteristic of the sensor.


Changes to any one of these three factors can negatively affect both the distinctiveness and the repeatability of the measurement, thus degrading the overall accuracy.

1.6 Data Storage

The data storage subsystem can vary as much as the biometric application itself.  Depending on the nature of the biometric authentication function, (comparing one-to-one biometric samples versus comparing one-to-many), the data storage function might reside on a smart card or in a central database.  In most cases, the data storage functions remains the same, involving the storage of a single or multiple users’ templates.  Another function entails the storage of raw biometric data, or “images,” which allows the biometric system to reconstruct corrupted templates from a user’s biometric data before the data enters the signal processing subsystem.  The storage of raw data allows the system vendor to make changes to the system data without the need to re-collect or “re-enroll” data from all users.

1.7 Templates

A biometric acquisition device in the form of a fingerprint reader or an iris scanner, for instance, attempts to capture an accurate image of the user’s biometric sample.  A second process converts the raw biometric into a small data file called a template. Some important characteristics of templates include:

  • Templates consist of a vendor’s mathematical representation of a user’s biometric sample derived from feature extractions of the user’s sample.
  • Templates are proprietary to each vendor and each biometric technology.  There is no common biometric template format; therefore, a template created in one vendor’s system cannot be used with another vendor’s system.  Since November, 2001, the International Committee for Information Technology Standards Technical Committee M1 has worked to establish common file formats and application program interfaces that address these template concerns.
  • No two templates are alike, even when created from the same biometric sample. For example, two successive placements of a user’s finger generates entirely different templates.
  • Template sizes vary from less than 9 bytes for voice print to more than 1000 bytes for  a facial image.
  • Templates can be stored in a local PC, a remote network server, smart card, or in the acquisition device itself.
  • Biometric data describing a user’s fingerprint or hand geometry, for example, cannot be reconstructed from biometric templates since the templates themselves consist of distinct features drawn from a biometric sample.
  • Enrollment templates stored in a one-to-many database may suffer from data corruption issues over time.

1.7.1 Match Template versus Enrollment Template

An important distinction exists between enrollment templates and match templates.  An enrollment template is created when a user first submits their biometric sample.  This enrollment template is then stored for future biometric template comparisons. In contrast, a match template is created during subsequent identification or verification attempts, where the match template is compared to the original enrollment template, and generally discarded after the comparison takes place.

1.8 Signal Processing

The signal processing subsystem performs its function in four phases: segmentation, feature extraction, quality control, and pattern matching.

1.8.1 Segmentation

Segmentation describes the process of removing unnecessary background information from the raw extracted data.  One example would be distortion in a voice channel; another example would be distortions produced by shadows or lighting affects for a facial scanning system.

1.8.2 Feature Extraction

With feature extraction, the signal processing must retrieve an accurate biometric pattern from the data and sensor characteristics as well as noise and signal loss imposed by the transmission process.  Given a quality image of the biometric pattern, the signal processing system preserves the distinct and repeatable data points while discarding data points deemed non-distinctive or redundant. Consider speech authentication, for example, where a voice verification engine might focus solely on the frequency relationship of vowels that depend on the speaker’s pronunciation and not on the word itself.  Think of feature extraction as non-reversible compression.  In other words, the original biometric sample cannot be reconstructed from the extracted biometric features.

1.8.3 Quality Control

Quality control involves a determination about whether or not the signal received from the data collection system before, during, or after feature extraction arrives with acceptable quality.  If the system determines the signal quality is insufficient, then the system will request a new sample from the data collection system.  This partially explains why biometric users may be asked to enroll their biometric characteristic more than once, potentially invoking a failure-to-enroll error.  Subsequent sections of this report explore the concept of enrollment in more detail. For now, understand that enrollment refers to storing a user’s biometric sample, or “template,” in a portable or centralized database.

1.8.4 Pattern Matching

The pattern matching process compares the user’s presented biometric feature (that has undergone the data collection, feature extraction, and quality control processes) with the user’s “previously enrolled” biometric feature stored in a database.

1.9 Biometric Matching

The concept of biometric matching speaks to the heart of biometric authentication and the accuracy associated with biometric technologies.  Biometric authentication deals in degrees of certainty and does not offer a 100% guarantee that a user’s biometric template will match a stored template in a given database. Instead, biometrics rely on a three step process built upon a given biometric product’s standards for scoring, threshold, and decision.  In this process, a user’s biometric template is assigned a specific value or score, which the biometric system compares to a pre-determined threshold setting used to decide whether the user’s template should be accepted or rejected.


By definition, the threshold represents a predefined number established by a system administrator for the purpose of establishing the necessary degree of correlation needed for the system to render a match/no match decision. If the user’s template score exceeds the threshold, it “passes,” and the system responds with a match.  The converse implies that the user’s template score “fails,” prompting the system to render a no match decision.  As with scoring, thresholds vary widely depending on the user’s security requirements and the specific biometric system deployed.


A decision simply represents the result of the comparison between the score and the threshold.  In addition to match and no match decisions, some biometric systems can also register an inconclusive decision based upon the system’s inability to match a user’s verification template with a poorly enrolled template.


Since no industry standardized scale exists to identify a uniform scoring methodology, vendors utilize their own proprietary scoring methodology to process templates and generate numeric values that can range from 10 to 100 or –1 to 1.  Recall that no two templates are exactly the same.  This partially explains why no biometric system can render a match/no match decision with 100% certainty.

1.10 Decision

The decision subsystem implements a predetermined system policy that dictates specific threshold criteria used to base a match / no-match decision, which ultimately leads to an accept/reject decision for the user.  The system policy should strive for a balance between stringent security settings and user-friendliness. In other words, a decision subsystem programmed to 99% accuracy might correctly reject 99% of all unauthorized users but also fail to accept a large percentage of legitimate, authorized users.  The converse is also true where a loosely defined decision will make the biometric system easy to use but also grant access for an unacceptable percentage of unauthorized users.

1.11 Transmission

Some biometric systems collect biometric data at one location and store the data at another.  This scenario requires a transmission channel to facilitate the information exchange.  With large amounts of data involved, (i.e., a large number of users and/or large file sizes), data compression techniques may be required to conserve bandwidth and storage space.  The process of compression and expansion can lead to quality degradation in the restored signal, depending on the nature of the biometric sample and the compression technique deployed.