Biometric Technologies and Ethics

  • Joseph Migga Kizza
Part of the Texts in Computer Science book series (TCS)


This chapter starts off by discussing. Biometric technologies as access and identification processes. Biometric technologies confirm a person’s identity by scanning physical characteristics such as a fingerprint, voice, eye movement, and facial recognition. We discuss how a typical biometric system operates in two distinct stages: the enrollment stage and the authentication stage. We also discuss biometric technologies traits used to confirm a person’s identity that include fingerprint, voice, eye movement, facial recognition, and a few others. Finally in discussing the ethical issues faced in the use of biometric technologies, we pose the following questions to the reader:
  • For respect for human dignity—In substituting names with codes, might biometrics degrade the human condition to that of animals or things?

  • For “informatization” of the human body—Is there any risk linked to the digitalization of human attributes and their distribution across the global information network?

  • For data protection and Privacy—What level of protection do biometric data deserve? Is there any risk related to the possible linkage of several biometric databases?

  • For respect for intimacy and body integrity—Is there any risk that biometrics may be felt as heavily intrusive technologies?

  • Biases based on human attributes:
    • Can ethnicity be derived from biometric data?

    • Do different ethnic types find it more or less difficult to use a particular biometric technique?

    • Is there any risk of discrimination based on ethnicity?

    • Is there any risk of categorization through profiling groups of people?

The answers to these questions inform our discussion.


Equal Error Rate Biometric Data Biometric System False Acceptance Rate Biometric Authentication 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

“BIOS(Life) and METRON(Measure) Measurement of any living entity [1]”

Learning Objectives

After reading this chapter, the reader should be able to:
  1. 1.

    Understand biometric science

  2. 2.

    Learn to use biometric data in access control

  3. 3.

    Understand how biometric data are used to fight crimes

  4. 4.

    Learn where and when biometric data can be used

  5. 5.

    Acquire the techniques required in biometric data acquisitions

  6. 6.

    Recognize the difficulties encountered in handling some biometric data in access control

  7. 7.

    Discuss and understand the ethical implications of biometric technologies


16.1 Introduction and Definitions

In the previous chapters, we have discussed the various types of computer crimes and how they are perpetuated. In  Chaps. 12 and  13, we have discussed the latest technologies to fight these crimes. In this chapter, we look at one of the latest and fastest-growing technologies to fight computer crimes. Biometric technology, based on human attributes, is perhaps one of the safest, most reliable, and most secure forms of access control so far in use. Access control technologies are among the top known and widely used security solutions and best practices. These technologies, as we have seen in Sect. 12.4.1, are based on three axioms:
  • Something you know—which includes all passwords and pass phrases.

  • Something you have—which includes all physical security passes such as pass cards and all sorts of access cards.

  • Something you are—which includes all human attributes. This is the group in which biometrics falls.

A biometric itself is a physical or psychological trait that can be measured, recorded, and quantified. Such traits are abundant in the human body, and in access control, they are used to do a biometric enrollment and stored in a database. The stored traits can then be used to compare with future traits collected from the same individual to confirm with a degree of certainty that this person is the same person, whose traits are in the database.

Biometric technologies confirm a person’s identity by scanning physical characteristics such as a fingerprint, voice, eye movement, and facial recognition. A typical biometric system operates in two distinct stages: the enrollment stage and the authentication stage. During enrollment, the physical traits of the subject are extracted, analyzed, and put in a digital form called a template and stored in a database.

To authenticate a user, the biometric data are once again acquired and processed, and a new template is created. The new template is matched against the template(s) stored in the database to identify a previously enrolled individual or to validate a claimed identity. Unlike other access control technologies, biometrics cannot be forgotten or stolen. Passwords are easily forgotten, and keys and cards can easily be lost or forcibly taken away from us.

16.1.1 Definitions

Before we proceed, let us give the basic definitions of the terms we are going to use throughout the chapter:
  • Enrollment is the recording of biometric traits resulting in the creation of a template.

  • A template is a digital representation of a physical trait. It is a long string of alphanumeric characters that describe, based on a biometric algorithm, characteristics or features of a physical trait. It is generated from the preprocessed data (features).

  • A biometric algorithm is a mathematical formula for turning the physical traits into a digital representation to form a template. It is also used in the matching of an enrolled template with a new template just created for verification or identification.

  • The first template created at enrollment is referred to as a stored template, and templates created for recognition and identification processes are called live templates.

  • When a stored and a live template are compared, the system calculates how closely they match. If the match is close enough, the person is verified or identified.

  • During verification and identification processes, two templates are compared. If two templates of two different individuals were to match, this is classified as false acceptance.

  • The probability of this happening is referred to as false acceptance rate (FAR).

  • If the live template fails to match an enrolled template for an individual, this is referred to as a false rejection. The probability of this happening is the false rejection rate (FRR).

  • Finally if an enrolled person fails to enroll to a biometric system, this is called failure to enroll (FTE).

Although there is a new interest in biometrics especially after September 11, 2001, biometrics use is as old as humanity itself. People have been identifying others like friends and adversaries for years using looks—facial, eyes and eye color, and fingerprints from medieval times. However, during the past several years and with heightened security, biometric technology has become increasingly popular. The technology, which can be used to permit access to a network or a building, has become an increasingly reliable, convenient, and cost-effective means of security.

Biometric technologies of the past, however, were very difficult, extremely intrusive, and not cost-effective. However, this has changed dramatically as advances in current technologies have made these technologies far less intrusive, highly invasive, and certainly cost-effective. This has made biometric access control much more practical than it has ever been in the past. Now a new generation of low-cost yet accurate fingerprint readers is available for most mobile applications so that screening stations can be put up in a few minutes. Although biometrics is one of those security control techniques that have been in use the longest, it does not have standards as yet. There is an array of services on the market for biometric devices to fit every form of security access control. Technological advances have resulted in smaller, high-quality, more accurate, and more reliable devices. Improvements in biometrics are essential because bad biometric security can lull system and network administrators into a false sense of safety. In addition, it can also lock out a legitimate user and admit an intruder. So, care must be taken when procuring biometric devices.

16.2 The Biometric Authentication Process

Before a biometric technique can be used as an access control technique for the system, first an enrollment process is initiated for each user to have his or her biometric data scanned by a biometric reader, processed to extract critical features, and then those features stored in a database as the user’s template. When a user requests access to a system resource, the user must be authenticated; the biometric readers verify customers’ identities by scanning their physical attributes, such as fingerprints, again. A match is sought by checking them against prints of the same attributes previously registered and stored in the database.

The key steps for this biometric authentication process are:
  • Image capture—using a biometric reader or scanner

  • Image recognition—based on a standard biometric algorithm

  • Template creation—again using a standard biometric algorithm and extracted features

  • Matching of the templates—both the live and the stored templates of the individual are compared for a match using a standard biometric algorithm

A standard biometric authentication used in the above phases usually comprises the following functional units:
  • Sensor device: a reader or scanner to acquire the biometric raw data from the individual. The reader or scanner can capture images from a fingerprint, a face, an iris, or a sound from a microphone. Readers or scanners at this stage may do some limited preprocessing without introducing foreign information or creating redundancy.

  • Feature extraction: to extract traits used in the creation of the template.

  • Matcher: to compare the live template with the stored reference template.

  • Reference archive: for storing the biometric reference templates.

One of the advantages that have made biometrics increasingly popular is that while other methods of access control such as firewalls and encryption are crucial to network security and provide a secure way to exchange information, they are still expensive and difficult to design for a comprehensive security system. Other access control techniques such as passwords, while inexpensive to implement, are easy to forget and easy to guess by unauthorized people if they are simple and too complex to be of any use if they are complex.

16.3 Biometric System Components

To function properly and perform those four functions above, all components of a biometric system must work in unison. These components are data acquisition, enrollment, signal processing, and decision policy.

16.3.1 Data Acquisition

The data acquisition component, the first of the biometric system components, captures the biometric traits presented to the system via a reader or a scanner. It then, as part of preprocessing, digitizes the raw data just captured. This process may be followed by data compression and parameterization, if needed. Finally, especially if data is to be moved over public communication channels, encryption of the data for added security is done.

16.3.2 Enrollments

If this is the first time a user’s biometric traits are presented to the biometric system for any reason, the enrollment process must be done first. As already indicated, this produces the stored template which will be used in all future matching for future biometric authentication. During this stage, the user submits data for template/model creation. A number of issues must be dealt with at this stage including the quality of the scans, the settings of the reader or scanner to account for the types of devices used, the environment, the types of users to be enrolled, and sometimes a parentage threshold for the failure to enroll rate, as well as the need for training before completing the enrollment.

16.3.3 Signal Processing

This is the stage where raw data just acquired are worked on to extract relevant and needed features that form a template. This is done by processing the data to remove noise and extract only that information that carries the needed features. Several other functions including normalization, segmentation, and quality assessment may be performed on the data as well.

16.3.4 Decision Policy

This final component performs matching functions that help in decision making. A choice must be made whether the decision will lead to verification or identification. Also both FAR and FRR must be noted and compared because they influence the final decision. Finally, the crossover rate must also be taken and the equal error rate (EER) is taken noting its threshold. It is no good if it is high.

16.4 Types of Biometric Technologies

As we have pointed out, biometric technologies confirm a person’s identity by scanning physical characteristics. These are technologies that vary depending on the traits used. There are a number of these traits including fingerprint, voice, eye movement, and facial recognition.

16.4.1 Finger Biometrics

Finger biometrics involves taking an individual’s fingerprints. The authentication process using fingerprints is referred to as fingerprint recognition. During the process, a user places his or her finger on a scanner or fingerprint reader. The reader captures a number of images of the finger imprint, usually the center of the finger. The center usually is the area with the richest unique features known as the minutiae. Fingerprints contain many of these unique minutiae forming ridges and valleys. These ridges and valleys form the basis for the loops, arches, and swirls that are characteristics of any fingerprint. From these minutiae, unique features are located and determined. There are two types of minutiae:
  • Ridge endings—the location where the ridge actually ends

  • Bifurcations—the location where a single ridge becomes two ridges

From these two types, the following subtypes emerge:
  • Bifurcation

  • Bridge

  • Double bifurcation

  • Dot

  • Opposed bifurcation

  • Island (short-ridge)

  • Hook (spur)

  • Lake (enclosure)

  • Ridge crossing

  • Ridge ending

  • Trifurcation

  • Opposed bifurcation (ridge ending)

Figure 16.1 shows a fingerprint and Fig. 16.2a–l shows many of these biometric features.
Fig. 16.1

Fingerprint basics

Fig. 16.2

(a) Bifurcation, (b) bridge, (c) dot, (d) double bifurcation, (e) opposed bifurcation, (f) island (short bridge), (g) hook (spur), (h) lake (enclosure), (i) ridge crossing, (j) opposed bifurcation, (k) ridge ending, (l) trifurcation (New South Wales Police Service.

The next stage in fingerprint capture is template creation. The unique features of the minutiae going into the template are extracted and identified. Along with these unique features of the minutiae, the location, position, as well as the type and quality of each minutia are also taken into consideration in the template creation stage. Finally after the template has been created, it is then stored. Upon presentation of a second template, the live template from an individual, the stored template is retrieved and matched to the live one. This is referred to as template matching. During this process the system tries to either verify or identify an ­individual whose template was presented. There are two categories of fingerprint matching techniques: minutiae-based and correlation-based.
  • In minutiae-based technique, the first minutia points are found and then they are mapped relative to their placement on the finger. This approach, however, has problems in matching different-sized (unregistered) minutia patterns. Part of the problem is that the method does not take into account the global variations of people’s pattern of ridges and furrows.

  • Correlation-based technique tries to overcome some of these difficulties. However, correlation-based processing has its own problems including requiring a precise location of a registration point, and it is also affected by image translation and rotation.

Modern, more reliable fingerprint processing techniques require sophisticated algorithms for reliable processing of the fingerprint image to eliminate noise, extract minutiae, be rotation- and translation-tolerant, and be as fast as possible for comfortable use in applications with large number of users.

Fingerprint recognition technology is perhaps one of the oldest biometric technologies. Fingerprint readers have been around for probably hundreds of years. These readers fall into two categories: mice with embedded sensors and stand-alone units. Although fingerprint technology is improving with current technology, making it possible to make a positive identification in a few seconds, fingerprint identification is susceptible to precision problems. Many fingerprints can result in false positives due to oil and skin problems on the subject’s finger. Also many of the latest fingerprint readers can be defeated by photos of fingerprints and 3D fingers from latent prints such as prints left on glass and other objects [2].

16.4.2 Hand Geometry

Hand geometry is an authentication technology that uses the geometric shape of the hand to identify a user. The technique works by measuring and then analyzing the shape and physical features of a user’s hand, such as finger length and width and palm width. Like fingerprints, this technique also uses a reader. To initiate the device, all users’ hands are read and measured and the statistics are stored in a database for future recognition. To activate the system the user places the palm of his or her hand on the surface of the reader. Readers usually have features that guide the user’s hand on the surface. Once on the surface, the hand, guided by the guiding features, is properly aligned for the reader to read off the hand’s attributes. The reader is hooked to a computer, usually a server, with an application that provides a live visual feedback of the top view and the side view of the hand. Hand features are extracted and taken as the defining feature vector of the user’s hand and then used to create a template. This template is then compared with the stored template created from the user’s hand at enrollment.

As human hands are not unique, individual hand features are not descriptive enough for proper identification; hence, hand biometric technology is not a very good technique for authentication without combining it with other techniques.

16.4.3 Face Biometrics

Like other human biometrics, facial biometrics are feature extraction, creating a template and then later creating another template of the subject whenever ­authentication of the subject is desired and then comparing the two templates. Facial biometric authentication utilizes the distinctive features of the subject’s face. These features are sometimes microfeatures that include:
  • Mouth

  • Nose

  • Eye

  • Cheekbones

  • Chin

  • Lips

  • Forehead

  • Ears

Additional features include upper outlines of the eye sockets, the areas surrounding the cheekbones, the sides of the mouth, and the location of the nose and eyes, as well as the distance between the eyes, the length of the nose, and the angle of the jaw. Typical sources of facial images include video recording and fixed cameras like digital camera. Once the image has been captured, the biometric algorithm is then used to create a template. The algorithm uses specific features, called eigenface, and special technologies including local feature analysis, neural networks, and automatic face processing.

An eigenface is a characteristic feature of a face which is literally an average face derived from statistical analysis of many pictures of the face [3]. The algorithm then takes these eigenface features and produces a unique file called a template. To authenticate an individual, the algorithm uses a newly created template from a recently captured facial image and compares that template with the stored template created from the image of the subject at enrollment. Face physical traits can be captured by either live scans or through use of photograph or videos. Like in hand biometrics, facial biometrics suffers from limitations including the fact that photo and video recording destroy the concept of depth. Because of this, some algorithms do not use them without an additional high-quality scanner or an additional biometrics.

16.4.4 Voice Biometrics

Very often we recognize friends and colleagues from their voices without having a visual of them first. This is the case because each individual has individual voice components called phonemes. Each phoneme has three unique parts: a pitch, a cadence, and an inflection. These then give each one of us a unique voice sound. This uniqueness in voice holds for different people although there are some seemingly close likenesses of voices from people who share cultural and regional identities. This closeness of sound is a result of form of accents.

During speech, referred to as phonating, an individual’s vocal folds produce a complex sound spectrum made up of a range of frequencies and overtones. As the spectrum travels through the various-sized areas in the vocal track, some of the frequencies resonate more than others. Larger spaces resonate at lower frequencies, while smaller ones at much higher frequencies. The throat and the mouth, the two largest spaces in the vocal track, produce the two lowest resonant frequencies called formants. These are resonant frequencies of the vocal tract produced when vowels are pronounced.

Linguists classify speech by looking at the characteristic formants in the spectrogram or frequency response of the speech. During speech, the human vocal track frequently opens and closes which causes changes in energy in all frequencies. In adult female the rate of repeated opening and closing can be high giving the sensation of a pitch in their speech.

Voice is captured by devices like a microphone or telephone. The quality of the captured voice depends on the recording device and the environment under which the recording is being done. After voice is captured, the voice algorithm being used by the voice biometrics then reduces each spoken word to segments composed of several dominant frequencies, the formants. The tones in each segment are then put into a digital format. The tones are then put into a template and stored. These tones in the template are collectively used to identify the subject’s unique voiceprint. Upon subsequent capture of a subject’s voice, another template is created and compared with the stored template created at enrollment for a match.

Voice recognition has been around for years; however, its real-life application has been slow because of the difficulties in deployment. Voice recognition is not a safe authentication technique because it can be fooled by recording types.

16.4.5 Handwriting Analysis

Another biometric that has been used for sometime is the handwriting analysis. Handwriting analysis can tell a lot about the personality of the subject. Graphology or handwriting analysis is a science of interpreting a person’s character from his/her personal handwriting. It is a scientific system of identifying and assessing the character and personality of a subject through a study of the subject’s handwriting. The techniques use well-defined and standardized methods to identify strokes and slants and relate them to specific personality traits. Further evaluation of and analysis of the strokes and slants along with psychology and knowledge of inner human behavior can lead to secrets about the hidden behavior of the subject. It can also lead to discovery of inner personal conflicts in the life of the subject.

16.4.6 Iris Biometrics

Perhaps the most outstanding and the most secure forms of biometrics are the iris and retina. An iris is that area of the eye where the pigmented or colored circle rings the dark pupil of the eye. The iris contains lots of interesting features including ligaments, furrows, ridges, crypts, rings, corona, freckles, and a zigzag collarette. According to Panko, iris authentication is the gold standard of all biometric authentications [2]. Iris technology uses either regular, small, high-quality cameras or infrared light into the eye of the user to scan and capture the features that exist in the colored tissue surrounding the pupil of the subject’s eye. This process takes only 1–2 s and provides the details of the iris that are mapped. Once the image is captured, the iris’ elastic connective tissue, the trabecular meshwork, is analyzed, processed into an optical fingerprint, translated into a digital form, and stored as a template.

Whenever a user wants access to a secure system, he or she looks in an iris reader. Modern iris readers can read a user’s eye up to 2 ft away. Verification time is short and it is getting shorter. Like in other eye scans, precautions must be taken to prevent a wrong person’s eyes from fooling the system. This is done by varying the light shone into the eye and then pupil dilations are recorded.

The use of iris scans for authentication is becoming popular, although it is a young technology. Its potential application areas include law enforcement agencies, border patrol and airports, and the financial sector, especially in banking.

16.4.7 Retina

Retina is a thin layer of cells at the back of the eyeball. It is the part of the eye responsible for converting light into nervous signals. Because of this, it contains photoreceptor cells which receive the light that it passes to the neural cell which produces neural signals. The signals are later processed by other neurons in the subsequent processes. The retina is also characterized by an abundance of unique patterns of the blood vessels. Every eye has its own totally unique pattern of blood vessels with distinctive traits including the eyes of identical twins. Because of this fact, retina biometrics is considered to be the best. The technology works by directing a low-intensity infrared light to capture the unique retina characteristics consisting of patterns of blood vessels on the thin nerve on the back of the eyeball that processes light entering through the pupil. These captured characteristics are then digitized and put into a template and stored, if it is the first time to capture the subject’s retina. The retina biometric technology is one of the smallest of all the biometric technologies and it is one of the most accurate and most reliable of the biometric technologies. However, the amount of effort and cooperation required of users has made it one of the least deployed of all the biometric technologies. Also the retina is small, internal, and difficult to measure which makes capturing its image more difficult than most biometric technologies. So despite retina accuracy, it is still often thought to be inconvenient and intrusive.

16.5 Ethical Implications of Biometric Technologies

Before we start a discussion of the ethical implications of biometric technologies, here are the questions to ponder. The answers to these questions will inform our discussion [1]:
  • For respect for human dignity—Does substituting names with codes and might biometrics degrade the human condition to that of animals?

  • For informatization of the human body—Is there any risk linked to the digitalization of human attributes and their distribution across the global information network?

  • For data protection and privacy—What level of protection does biometric data deserve? Is there any risk related to the possible linkage of several biometric databases?

  • For respect for intimacy and body integrity—Is there any risk that biometrics may be felt as heavily intrusive technologies?

  • Biases based on human attributes:
    • Can ethnicity be derived from biometric data?

    • Do different ethnic types find it more or less difficult to use a particular biometric technique?

    • Is there any risk of discrimination based on ethnicity?

    • Is there any risk of categorization through profiling groups of people?

All the answers to the above questions seem to point to a summary of a dialogue about human dignity. The fundamental and core essence of human existence and human life is bound in the respect and dignity of the human body. The human body and all its attributes is a bastion of the components of the human being and as such should be accorded the fundamental conditions for human freedom and equality. So any form and process that uses any human bodily attributes must make every effort to respect human dignity in any situation. As Emilio Mordini(a) and Carlo Petrini [4] point out in Ethical and social implications of biometric identification technology, as biometric identification devices become more pervasive, there is a growing likelihood that they may compromise individual privacy in a deep and thorough fashion. This is so because biometrics can reveal more about individuals than they are willing to give about themselves. Are we ready for this form of being digital? Are we ready for a centralized digitalized self stored for everyone to see?

Discussion Topic: Look up the RISE project

RISE (Rising pan-European and International Awareness on Biometric and Security Ethics) by the European Commission. Reference:

16.6 The Future of Biometrics

The current biometric technologies are all characterized by three or four processes: the image capture, feature extraction, template creation, and the comparison. These processes are used in the biometric quest for authentication. The biometric authentication system itself consists of two phases: enrollment and matching. In the enrollment phase, a subject interacts with the biometric system where one or more of the subject’s selected physical characteristics are captured by the system. The characteristic features captured are then processed by a numerical algorithm and entered into a database. To create an entry into the database, the algorithm creates a template, a digital representation of the obtained biometric.

On subsequent subject encounter with the system, the process of acquiring the biometrics and digitizing it is again performed. And a template is again created. But instead of storing the new template of the subject, it is compared with the subject’s stored template for authentication. The comparison process employs a Hamming distance measure, a mathematical technique which measures how similar two-bit strings are.

The measure of performance of a biometric is based on three concepts which we referred to earlier as FAR, false nonmatch or reject rate (FRR), and failure to enroll rate (FTE or FER). Biometric performance is commonly the rate at which both accept and reject errors are equal. This rate is referred to as the EER. We want EER to be as low as possible for a good biometric algorithm. Advances in technology and the great security awareness that we are currently experiencing are driving the use of biometric technologies to new heights. With increasing miniaturization, price reduction, ease of use, less intrusiveness, and more invasiveness, the future of biometric technology seems brighter than ever before.

However good the biometrics are, they can still fall victim of identity theft, for example, when one gets access and modifies or changes the stored template. Also privacy concerns on the personal information collected from the subjects which may diminish personal liberties. Finally as the popularity of the technologies increases, more private concerns are on the rise that these technologies may cause physical harm to users.

Biometric Discussion Questions

Biometrics: A grand challenge

A discussion

To prepare for the discussion, read the following paper before coming to class. Biometrics:

A grand challenge found at:

The paper discusses several problems facing the biometric technology including:
  1. 1.

    How to accurately and efficiently represent and recognize biometric patterns?

  2. 2.

    How to guarantee that the sensed measurements are not fraudulent?

  3. 3.

    How to make sure that the application is indeed exclusively using pattern recognition for the expressed purpose?

  4. 4.

    How to acquire repeatable and distinctive patterns from a broad population?


Unless all these questions are satisfactorily answered, the future of biometrics is not going to take off as many currently believe.

Divide the class into groups. Within your group, discuss and take notes to present to the full class on the question assigned to your group. The questions are assigned as follows:
  • Group 1. How to accurately and efficiently represent and recognize biometric patterns?

  • Group 2. How to guarantee that the sensed measurements are not fraudulent?

  • Group 3. How to make sure that the application is indeed exclusively using pattern recognition for the expressed purpose?

  • Group 4. How to acquire repeatable and distinctive patterns from a broad population?


  1. 1.

    What is a biometric? List the different characteristics for a chosen biometric.

  2. 2.

    Discuss the basic steps in correcting biometric data.

  3. 3.

    List and discuss the standard techniques and tools used in biometric data gathering.

  4. 4.

    List the different types of biometric data.

  5. 5.

    Differentiate between online and digital biometric data?

  6. 6.

    Discuss why it is so important to handle some types of biometric data with care.

  7. 7.

    Grade and list the different biometrics listed above in ascending order of trust.

  8. 8.

    Why are some biometrics more trustworthy than others?

  9. 9.

    What role does ethics play during biometric data collection and use in access control?

  10. 10.

    Discuss the following statement: “Fingerprints as biometric data are the most widely used, yet they are the least trusted.”

  11. 11.

    Discuss the future of biometrics in access control.

  12. 12.

    What role does biometrics play in fighting crime? Discuss how this is done.

  13. 13.

    Discuss the future of biometrics in crime investigation.

  14. 14.

    Discuss the following statement: “The most trusted biometrics are the most expensive to extract.”

  15. 15.

    Discuss an incident you have heard or witnessed in which biometric data for either access control or as evidence.

  16. 16.

    List and discuss cases both criminal and civil in which biometric evidence might be involved.



  1. 1.
    Venier S (2009) Ethical aspects of biometric identification technologies in a multicultural society. AECME annual meeting, Venice, 10–11 September, 2009Google Scholar
  2. 2.
    Panko RR (2004) Corporate computer and network security. Prentice-Hall, Upper Saddle RiverGoogle Scholar
  3. 3.
  4. 4.
    Mordini E, Carlo C (2007) Ethical and social implications of biometric identification technology. Ann Ist Super Sanità 43(1):5–11Google Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  • Joseph Migga Kizza
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of TennesseeChattanoogaUSA

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