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`IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 14, NO. 1, JANUARY 2004
`
`An Introduction to Biometric Recognition
`
`Anil K. Jain, Fellow, IEEE, Arun Ross, Member, IEEE, and Salil Prabhakar, Member, IEEE
`
`Invited Paper
`
`Abstract—A wide variety of systems requires reliable personal
`recognition schemes to either confirm or determine the identity
`of an individual requesting their services. The purpose of such
`schemes is to ensure that the rendered services are accessed only
`by a legitimate user and no one else. Examples of such applications
`include secure access to buildings, computer systems, laptops,
`cellular phones, and ATMs. In the absence of robust personal
`recognition schemes, these systems are vulnerable to the wiles of
`an impostor. Biometric recognition or, simply, biometrics refers
`to the automatic recognition of individuals based on their physi-
`ological and/or behavioral characteristics. By using biometrics, it
`is possible to confirm or establish an individual’s identity based
`on “who she is,” rather than by “what she possesses” (e.g., an ID
`card) or “what she remembers” (e.g., a password). In this paper,
`we give a brief overview of the field of biometrics and summarize
`some of its advantages, disadvantages, strengths, limitations, and
`related privacy concerns.
`Index Terms—Biometrics, identification, multimodal biomet-
`rics, recognition, verification.
`
`I. INTRODUCTION
`
`H UMANS have used body characteristics such as face,
`
`voice, and gait for thousands of years to recognize each
`other. Alphonse Bertillon, chief of the criminal identification
`division of the police department in Paris, developed and then
`practiced the idea of using a number of body measurements to
`identify criminals in the mid-19th century. Just as his idea was
`gaining popularity, it was obscured by a far more significant
`and practical discovery of the distinctiveness of the human
`fingerprints in the late 19th century. Soon after this discovery,
`many major law enforcement departments embraced the idea
`of first “booking” the fingerprints of criminals and storing it in
`a database (actually, a card file). Later, the leftover (typically,
`fragmentary) fingerprints (commonly referred to as latents)
`at the scene of crime could be “lifted” and matched with
`fingerprints in the database to determine the identity of the
`criminals. Although biometrics emerged from its extensive use
`in law enforcement to identify criminals (e.g., illegal aliens,
`
`security clearance for employees for sensitive jobs, fatherhood
`determination, forensics, and positive identification of convicts
`and prisoners), it is being increasingly used today to establish
`person recognition in a large number of civilian applications.
`What biological measurements qualify to be a biometric?
`Any human physiological and/or behavioral characteristic can
`be used as a biometric characteristic as long as it satisfies the
`following requirements:
`(cid:127) Universality: each person should have the characteristic.
`(cid:127) Distinctiveness: any two persons should be sufficiently
`different in terms of the characteristic.
`(cid:127) Permanence:
`the characteristic should be sufficiently
`invariant (with respect to the matching criterion) over a
`period of time.
`(cid:127) Collectability: the characteristic can be measured quanti-
`tatively.
`However, in a practical biometric system (i.e., a system that em-
`ploys biometrics for personal recognition), there are a number
`of other issues that should be considered, including:
`(cid:127) performance, which refers to the achievable recognition
`accuracy and speed, the resources required to achieve the
`desired recognition accuracy and speed, as well as the op-
`erational and environmental factors that affect the accu-
`racy and speed;
`(cid:127) acceptability, which indicates the extent to which people
`are willing to accept the use of a particular biometric iden-
`tifier (characteristic) in their daily lives;
`(cid:127) circumvention, which reflects how easily the system can
`be fooled using fraudulent methods.
`A practical biometric system should meet the specified recogni-
`tion accuracy, speed, and resource requirements, be harmless to
`the users, be accepted by the intended population, and be suffi-
`ciently robust to various fraudulent methods and attacks to the
`system.
`
`II. BIOMETRIC SYSTEMS
`
`Manuscript received January 30, 2003; revised May 13, 2003. This paper was
`previously published in part in the IEEE Security Privacy Magazine and the
`Handbook of Fingerprint Recognition.
`A. K. Jain is with the Department of Computer Science and Engi-
`neering, Michigan State University, East Lansing, MI 48824 USA (e-mail:
`jain@cse.msu.edu).
`A. Ross is with the Lane Department of Computer Science and Electrical
`Engineering, West Virginia University, Morgantown, WV 26506 USA (e-mail:
`ross@csee.wvu.edu).
`S. Prabhakar is with the Algorithms Research Group, DigitalPersona Inc.,
`Redwood City, CA 94063 USA (e-mail: salilp@digitalpersona.com).
`Digital Object Identifier 10.1109/TCSVT.2003.818349
`
`A biometric system is essentially a pattern recognition system
`that operates by acquiring biometric data from an individual, ex-
`tracting a feature set from the acquired data, and comparing this
`feature set against the template set in the database. Depending
`on the application context, a biometric system may operate ei-
`ther in verification mode or identification mode.
`(cid:127) In the verification mode, the system validates a person’s
`identity by comparing the captured biometric data with her
`own biometric template(s) stored in the system database.
`
`1051-8215/04$20.00 © 2004 IEEE
`
`GTL 1008
`IPR of U.S. Patent 6,636,591
`
`

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`JAIN et al.: AN INTRODUCTION TO BIOMETRIC RECOGNITION
`
`5
`
`Fig. 1. Block diagrams of enrollment, verification, and identification tasks are shown using the four main modules of a biometric system, i.e., sensor, feature
`extraction, matcher, and system database.
`
`In such a system, an individual who desires to be recog-
`nized claims an identity, usually via a personal identifi-
`cation number (PIN), a user name, or a smart card, and
`the system conducts a one-to-one comparison to determine
`whether the claim is true or not (e.g., “Does this biometric
`data belong to Bob?”). Identity verification is typically
`used for positive recognition, where the aim is to prevent
`multiple people from using the same identity [26].
`(cid:127) In the identification mode, the system recognizes an indi-
`vidual by searching the templates of all the users in the
`database for a match. Therefore, the system conducts a
`one-to-many comparison to establish an individual’s iden-
`tity (or fails if the subject is not enrolled in the system data-
`base) without the subject having to claim an identity (e.g.,
`“Whose biometric data is this?”). Identification is a crit-
`ical component in negative recognition applications where
`the system establishes whether the person is who she (im-
`plicitly or explicitly) denies to be. The purpose of nega-
`tive recognition is to prevent a single person from using
`multiple identities [26]. Identification may also be used in
`positive recognition for convenience (the user is not re-
`quired to claim an identity). While traditional methods of
`personal recognition such as passwords, PINs, keys, and
`
`tokens may work for positive recognition, negative recog-
`nition can only be established through biometrics.
`Throughout this paper, we will use the generic term recogni-
`tion where we do not wish to make a distinction between veri-
`fication and identification. The block diagrams of a verification
`system and an identification system are depicted in Fig. 1; user
`enrollment, which is common to both of the tasks, is also graph-
`ically illustrated.
`The verification problem may be formally posed as follows:
`(extracted from the biometric
`given an input feature vector
`data) and a claimed identity , determine if (
`) belongs to
`class
`or
`, where
`indicates that the claim is true (a gen-
`indicates that the claim is false (an impostor).
`uine user) and
`is matched against
`, the biometric template
`Typically,
`, to determine its category. Thus
`corresponding to user
`
`if
`otherwise
`
`is the function that measures the similarity between
`where
`feature vectors
`and
`, and is a predefined threshold. The
`is termed as a similarity or matching score be-
`value
`tween the biometric measurements of the user and the claimed
`identity. Therefore, every claimed identity is classified into
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`6
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`IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 14, NO. 1, JANUARY 2004
`
`, and and the function
`,
`,
`based on the variables
`or
`. Note that biometric measurements (e.g., fingerprints) of the
`same individual taken at different times are almost never iden-
`tical. This is the reason for introducing the threshold .
`The identification problem, on the other hand, may be stated
`, determine the
`as follows. Given an input feature vector
`,
`. Here
`are
`identity
`the identities enrolled in the system and
`indicates the re-
`ject case where no suitable identity can be determined for the
`user. Hence
`
`if
`otherwise
`
`is the biometric template corresponding to identity
`where
`, and is a predefined threshold.
`A biometric system is designed using the following four main
`modules (see Fig. 1).
`1) Sensor module, which captures the biometric data of an
`individual. An example is a fingerprint sensor that images
`the ridge and valley structure of a user’s finger.
`2) Feature extraction module,
`in which the acquired
`biometric data is processed to extract a set of salient or
`discriminatory features. For example, the position and
`orientation of minutiae points (local ridge and valley
`singularities) in a fingerprint
`image are extracted in
`the feature extraction module of a fingerprint-based
`biometric system.
`3) Matcher module, in which the features extracted during
`recognition are compared against the stored templates to
`generate matching scores. For example, in the matching
`module of a fingerprint-based biometric system,
`the
`number of matching minutiae between the input and the
`template fingerprint images is determined and a matching
`score is reported. The matcher module also encapsulates
`a decision making module, in which a user’s claimed
`identity is confirmed (verification) or a user’s identity is
`established (identification) based on the matching score.
`4) System database module, which is used by the biometric
`system to store the biometric templates of the enrolled
`users. The enrollment module is responsible for enrolling
`individuals into the biometric system database. During
`the enrollment phase, the biometric characteristic of an
`individual is first scanned by a biometric reader to pro-
`duce a digital representation of the characteristic. The
`data capture during the enrollment process may or may
`not be supervised by a human depending on the appli-
`cation. A quality check is generally performed to ensure
`that the acquired sample can be reliably processed by suc-
`cessive stages. In order to facilitate matching, the input
`digital representation is further processed by a feature ex-
`tractor to generate a compact but expressive representa-
`tion, called a template. Depending on the application, the
`template may be stored in the central database of the bio-
`metric system or be recorded on a smart card issued to the
`individual. Usually, multiple templates of an individual
`are stored to account for variations observed in the bio-
`metric trait and the templates in the database may be up-
`dated over time.
`
`III. BIOMETRIC SYSTEM ERRORS
`
`Two samples of the same biometric characteristic from the
`same person (e.g., two impressions of a user’s right index
`finger) are not exactly the same due to imperfect imaging
`conditions (e.g., sensor noise and dry fingers), changes in the
`user’s physiological or behavioral characteristics (e.g., cuts and
`bruises on the finger), ambient conditions (e.g., temperature
`and humidity), and user’s interaction with the sensor (e.g.,
`finger placement). Therefore,
`the response of a biometric
`(typically a
`matching system is the matching score
`single number) that quantifies the similarity between the input
`) and the template (
`) representations. The higher the
`(
`score, the more certain is the system that the two biometric
`measurements come from the same person. The system deci-
`sion is regulated by the threshold : pairs of biometric samples
`generating scores higher than or equal to are inferred as mate
`pairs (i.e., belonging to the same person); pairs of biometric
`samples generating scores lower than are inferred as nonmate
`pairs (i.e., belonging to different persons). The distribution of
`scores generated from pairs of samples from the same person
`is called the genuine distribution and from different persons is
`called the impostor distribution [see Fig. 2(a)].
`A biometric verification system makes two types of errors:
`1) mistaking biometric measurements from two different per-
`sons to be from the same person (called false match) and 2) mis-
`taking two biometric measurements from the same person to be
`from two different persons (called false nonmatch). These two
`types of errors are often termed as false accept and false reject,
`respectively. There is a tradeoff between false match rate (FMR)
`and false nonmatch rate (FNMR) in every biometric system. In
`fact, both FMR and FNMR are functions of the system threshold
`; if
`is decreased to make the system more tolerant to input vari-
`is
`ations and noise, then FMR increases. On the other hand, if
`raised to make the system more secure, then FNMR increases
`accordingly. The system performance at all the operating points
`(thresholds ) can be depicted in the form of a receiver oper-
`ating characteristic (ROC) curve. A ROC curve is a plot of FMR
`[see
`against (1-FNMR) or FNMR for various threshold values
`Fig. 2(b)].
`Mathematically, the errors in a verification system can be for-
`mulated as follows. If the stored biometric template of the user
`is represented by
`and the acquired input for recognition is
`represented by
`, then the null and alternate hypotheses are:
`input
`does not come from the same person as the
`template
`;
`input
`comes from the same person as the template
`The associated decisions are as follows:
`person is not who she claims to be;
`person is who she claims to be.
`The decision rule is as follows. If the matching score
`is less than the system threshold , then decide
`, else decide
`. The above terminology is borrowed from
`communication theory, where the goal is to detect a message
`in the presence of noise.
`is the hypothesis that the received
`signal is noise alone, and
`is the hypothesis that the received
`
`.
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`JAIN et al.: AN INTRODUCTION TO BIOMETRIC RECOGNITION
`
`7
`
`Fig. 2. Biometric system error rates. (a) FMR and FNMR for a given threshold t are displayed over the genuine and impostor score distributions; FMR is the
`percentage of nonmate pairs whose matching scores are greater than or equal to t, and FNMR is the percentage of mate pairs whose matching scores are less than t.
`(b) Choosing different operating points results in different FMR and FNMR. The curve relating FMR to FNMR at different thresholds is referred to as receiver
`operating characteristics (ROC). Typical operating points of different biometric applications are displayed on an ROC curve. Lack of understanding of the error
`rates is a primary source of confusion in assessing system accuracy in vendor/user communities alike.
`
`signal is message plus the noise. Such a hypothesis testing
`formulation inherently contains two types of errors.
`Type I:
`is decided when
`is true);
`false match (
`Type II: false nonmatch (
`is decided when
`is true).
`FMR is the probability of type-I error (also called significance
`level in hypothesis testing) and FNMR is the probability of
`type-II error as
`
`The expression (1-FNMR) is also called the power of the hy-
`pothesis test. To evaluate the accuracy of a fingerprint biometric
`system, one must collect scores generated from multiple im-
`ages of the same finger (the distribution
`,
`and scores generated from a number of images from different
`fingers (the distribution
`. Fig. 2(a) graphi-
`cally illustrates the computation of FMR and FNMR over gen-
`uine and impostor distributions
`
`Besides the above error rates, the failure to capture (FTC) rate
`and the failure to enroll (FTE) rate are also used to summarize
`the accuracy of a biometric system. The FTC rate is only ap-
`plicable when the biometric device has an automatic capture
`functionality implemented in it and denotes the percentage of
`times the biometric device fails to capture a sample when the
`biometric characteristic is presented to it. This type of error typ-
`ically occurs when the device is not able to locate a biometric
`signal of sufficient quality (e.g., an extremely faint fingerprint or
`an occluded face). The FTE rate, on the other hand, denotes the
`percentage of times users are not able to enroll in the recognition
`system. There is a tradeoff between the FTE rate and the per-
`
`ceived system accuracy (FMR and FNMR). FTE errors typically
`occur when the system rejects poor quality inputs during en-
`rollment. Consequently, the database contains only good quality
`templates and the perceived system accuracy improves. Because
`of the interdependence among the failure rates and error rates,
`all these rates (i.e., FTE, FTC, FNMR, FMR) constitute impor-
`tant specifications in a biometric system, and should be reported
`during performance evaluation.
`The accuracy of a biometric system in the identification mode
`can be inferred using the system accuracy in the verification
`mode under simplifying assumptions. Let us denote the iden-
`tification false nonmatch and false match rates with
`and
`, respectively, where
`represents the number of
`identities in the system database (for simplicity, we assume that
`only a single identification attempt is made per subject, a single
`biometric template is used for each enrolled user, and the im-
`postor scores between different users are uncorrelated). Then,
`and
`(the approximations hold good only when
`). A detailed discussion on these issues is available in [25]
`and [27].
`If the templates in the database of an identification system
`have been classified and indexed, then only a portion of the data-
`base is searched during identification and this leads to the fol-
`lowing formulation of
`and
`.
`(cid:127)
`
`, where RER
`(retrieval error rate) is the probability that the database
`template corresponding to the searched finger is wrongly
`discarded by the retrieval mechanism. The above expres-
`sion is obtained using the following argument: in case
`the template is not correctly retrieved (this happens with
`probability RER), the system always generates a false-non
`match, whereas in case the retrieval returns the right tem-
`plate [this happens with probability (1-RER)], false non-
`match rate of the system is FNMR. Also, this expression is
`
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`IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 14, NO. 1, JANUARY 2004
`
`Fig. 3. Examples of biometric characteristics: (a) DNA, (b) ear, (c) face, (d) facial thermogram, (e) hand thermogram, (f) hand vein, (g) fingerprint, (h) gait,
`(i) hand geometry, (j) iris, (k) palmprint, (l) retina, (m) signature, and (n) voice.
`
`only an approximation since it does not consider the prob-
`ability of falsely matching an incorrect template before the
`right one is retrieved [28].
`
`(cid:127)
`
`(also called
`, where
`the penetration rate) is the average percentage of database
`searched during the identification of an input fingerprint.
`The accuracy requirements of a biometric system are very
`much application-dependent. For example, in some forensic ap-
`plications such as criminal identification, one of the critical de-
`sign issues is the FNMR rate (and not the FMR), i.e., we do not
`want to miss identifying a criminal even at the risk of manually
`examining a large number of potentially incorrect matches gen-
`erated by the biometric system. On the other extreme, the FMR
`may be one of the most important factors in a highly secure ac-
`cess control application, where the primary objective is deter-
`ring impostors (although we are concerned with the possible in-
`convenience to the legitimate users due to a high FNMR). There
`are a number of civilian applications whose performance re-
`quirements lie in between these two extremes, where both FMR
`and FNMR need to be considered. For example, in applications
`like bank ATM card verification, a false match means a loss of
`several hundred dollars while a high FNMR may lead to a po-
`tential loss of a valued customer. Fig. 2(b) depicts the FMR and
`FNMR tradeoffs in different types of biometric applications.
`
`IV. COMPARISON OF VARIOUS BIOMETRICS
`
`A number of biometric characteristics exist and are in use
`in various applications (see Fig. 3). Each biometric has its
`
`strengths and weaknesses, and the choice depends on the
`application. No single biometric is expected to effectively
`meet the requirements of all the applications. In other words,
`no biometric is “optimal.” The match between a specific
`biometric and an application is determined depending upon the
`operational mode of the application and the properties of the
`biometric characteristic. A brief introduction to the commonly
`used biometrics is given below.
`(cid:127) DNA: Deoxyribonucleic acid (DNA)
`is the one-di-
`mensional (1–D) ultimate unique code for one’s in-
`dividuality—except
`for
`the fact
`that
`identical
`twins
`have identical DNA patterns. It is, however, currently
`used mostly in the context of forensic applications for
`person recognition. Three issues limit the utility of this
`biometrics for other applications: 1) contamination and
`sensitivity: it is easy to steal a piece of DNA from an
`unsuspecting subject that can be subsequently abused for
`an ulterior purpose; 2) automatic real-time recognition
`issues: the present technology for DNA matching requires
`cumbersome chemical methods (wet processes) involving
`an expert’s skills and is not geared for on-line noninvasive
`recognition; and 3) privacy issues: information about
`susceptibilities of a person to certain diseases could be
`gained from the DNA pattern and there is a concern that
`the unintended abuse of genetic code information may
`result in discrimination, e.g., in hiring practices.
`(cid:127) Ear: It has been suggested that the shape of the ear and
`the structure of the cartilegenous tissue of the pinna are
`distinctive. The ear recognition approaches are based on
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`JAIN et al.: AN INTRODUCTION TO BIOMETRIC RECOGNITION
`
`9
`
`matching the distance of salient points on the pinna from a
`landmark location on the ear. The features of an ear are not
`expected to be very distinctive in establishing the identity
`of an individual.
`(cid:127) Face: Face recognition is a nonintrusive method, and fa-
`cial images are probably the most common biometric char-
`acteristic used by humans to make a personal recogni-
`tion. The applications of facial recognition range from a
`static, controlled “mug-shot” verification to a dynamic,
`uncontrolled face identification in a cluttered background
`(e.g., airport). The most popular approaches to face recog-
`nition are based on either: 1) the location and shape of
`facial attributes such as the eyes, eyebrows, nose, lips
`and chin, and their spatial relationships, or 2) the overall
`(global) analysis of the face image that represents a face
`as a weighted combination of a number of canonical faces.
`While the verification performance of the face recogni-
`tion systems that are commercially available is reasonable
`[34], they impose a number of restrictions on how the fa-
`cial images are obtained, sometimes requiring a fixed and
`simple background or special illumination. These systems
`also have difficulty in recognizing a face from images cap-
`tured from two drastically different views and under dif-
`ferent illumination conditions. It is questionable whether
`the face itself, without any contextual information, is a suf-
`ficient basis for recognizing a person from a large number
`of identities with an extremely high level of confidence
`[29]. In order for a facial recognition system to work well
`in practice, it should automatically: 1) detect whether a
`face is present in the acquired image; 2) locate the face
`if there is one; and 3) recognize the face from a general
`viewpoint (i.e., from any pose).
`(cid:127) Facial, hand, and hand vein infrared thermogram: The
`pattern of heat radiated by human body is a character-
`istic of an individual and can be captured by an infrared
`camera in an unobtrusive way much like a regular (visible
`spectrum) photograph. The technology could be used for
`covert recognition. A thermogram-based system does not
`require contact and is noninvasive, but image acquisition is
`challenging in uncontrolled environments, where heat em-
`anating surfaces (e.g., room heaters and vehicle exhaust
`pipes) are present in the vicinity of the body. A related
`technology using near infrared imaging is used to scan
`the back of a clenched fist to determine hand vein struc-
`ture. Infrared sensors are prohibitively expensive which is
`a factor inhibiting wide spread use of the thermograms.
`(cid:127) Fingerprint: Humans have used fingerprints for personal
`identification for many centuries and the matching accu-
`racy using fingerprints has been shown to be very high
`[25]. A fingerprint is the pattern of ridges and valleys on
`the surface of a fingertip, the formation of which is deter-
`mined during the first seven months of fetal development.
`Fingerprints of identical twins are different and so are the
`prints on each finger of the same person. Today, a finger-
`print scanner costs about U.S. $20 when ordered in large
`quantities and the marginal cost of embedding a finger-
`print-based biometric in a system (e.g., laptop computer)
`has become affordable in a large number of applications.
`
`The accuracy of the currently available fingerprint recog-
`nition systems is adequate for verification systems and
`small- to medium-scale identification systems involving a
`few hundred users. Multiple fingerprints of a person pro-
`vide additional information to allow for large-scale recog-
`nition involving millions of identities. One problem with
`the current fingerprint recognition systems is that they
`require a large amount of computational resources, es-
`pecially when operating in the identification mode. Fi-
`nally, fingerprints of a small fraction of the population may
`be unsuitable for automatic identification because of ge-
`netic factors, aging, environmental, or occupational rea-
`sons (e.g., manual workers may have a large number of
`cuts and bruises on their fingerprints that keep changing).
`(cid:127) Gait: Gait is the peculiar way one walks and is a complex
`spatio-temporal biometric. Gait is not supposed to be very
`distinctive, but is sufficiently discriminatory to allow veri-
`fication in some low-security applications. Gait is a behav-
`ioral biometric and may not remain invariant, especially
`over a long period of time, due to fluctuations in body
`weight, major injuries involving joints or brain, or due to
`inebriety. Acquisition of gait is similar to acquiring a facial
`picture and, hence, may be an acceptable biometric. Since
`gait-based systems use the video-sequence footage of a
`walking person to measure several different movements
`of each articulate joint, it is input intensive and computa-
`tionally expensive.
`(cid:127) Hand and finger geometry: Hand geometry recognition
`systems are based on a number of measurements taken
`from the human hand, including its shape, size of palm,
`and lengths and widths of the fingers. Commercial hand
`geometry-based verification systems have been installed
`in hundreds of locations around the world. The technique
`is very simple, relatively easy to use, and inexpensive.
`Environmental factors such as dry weather or individual
`anomalies such as dry skin do not appear to have any
`negative effects on the verification accuracy of hand ge-
`ometry-based systems. The geometry of the hand is not
`known to be very distinctive and hand geometry-based
`recognition systems cannot be scaled up for systems re-
`quiring identification of an individual from a large pop-
`ulation. Further, hand geometry information may not be
`invariant during the growth period of children. In addi-
`tion, an individual’s jewelry (e.g., rings) or limitations
`in dexterity (e.g., from arthritis), may pose further chal-
`lenges in extracting the correct hand geometry informa-
`tion. The physical size of a hand geometry-based system
`is large, and it cannot be embedded in certain devices like
`laptops. There are verification systems available that are
`based on measurements of only a few fingers (typically,
`index and middle) instead of the entire hand. These de-
`vices are smaller than those used for hand geometry, but
`still much larger than those used in some other biometrics
`(e.g., fingerprint, face, voice).
`(cid:127) Iris: The iris is the annular region of the eye bounded by
`the pupil and the sclera (white of the eye) on either side.
`The visual texture of the iris is formed during fetal devel-
`opment and stabilizes during the first two years of life. The
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`IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 14, NO. 1, JANUARY 2004
`
`complex iris texture carries very distinctive information
`useful for personal recognition. The accuracy and speed
`of currently deployed iris-based recognition systems is
`promising and point to the feasibility of large-scale iden-
`tification systems based on iris information. Each iris is
`distinctive and, like fingerprints, even the irises of iden-
`tical twins are different. It is extremely difficult to surgi-
`cally tamper the texture of the iris. Further, it is rather easy
`to detect artificial irises (e.g., designer contact lenses). Al-
`though, the early iris-based recognition systems required
`considerable user participation and were expensive, the
`newer systems have become more user-friendly and cost-
`effective.
`(cid:127) Keystroke: It is hypothesized that each person types on
`a keyboard in a characteristic way. This behavioral bio-
`metric is not expected to be unique to each individual but
`it offers sufficient discriminatory information to permit
`identity verification. Keystroke dynamics is a behavioral
`biometric; for some individuals, one may expect to ob-
`serve large variations in typical typing patterns. Further,
`the keystrokes of a person using a system could be mon-
`itored unobtrusively as that person is keying in informa-
`tion.
`(cid:127) Odor: It is known that each object exudes an odor that is
`characteristic of its chemical composition and this could
`be used for distinguishing various objects. A whiff of air
`surrounding an object is blown over an array of chem-
`ical sensors, each sensitive to a certain group of (aromatic)
`compounds. A component of the odor emitted by a human
`(or any animal) body is distinctive to a particular indi-
`vidual. It is not clear if the invariance in the body odor
`could be detected despite deodorant smells, and varying
`chemical composition of the surrounding environment.
`(cid:127) Palmprint: The palms of the human hands contain pattern
`of ridges and valleys much like the fingerprints. The area
`of the palm is much larger than the area of a finger and, as
`a result, palmprints are expected to be even more distinc-
`tive than the fingerprints. Since palmprint scanners need
`to capture a large area, they are bulkier and more expen-
`sive than the fingerprint sensors. Human palms also con-
`tain additional distinctive features such as principal lines
`and wrinkles that can be captured even with a lower resolu-
`tion scanner, which would be cheaper [32]. Finally, when
`using a high-resolution palmprint scanner, all the features
`of the palm such as hand geometry, ridge and valley fea-
`tures (e.g., minutiae and singular points such as deltas),
`principal lines, and wrinkles may be combined to build a
`highly accurate biometric system.
`(cid:127) Retinal scan: The retinal vasculature is rich in structure
`and is supposed to be a characteristic of each individual
`and each eye. It is claimed to be the most secure biometric
`since it is not easy to change or replicate the retinal vas-
`culature. The image acquisition requires a person to peep
`into an eye-piece and focus on a specific spot in the vi-
`sual field so that a predetermined part of the retinal vas-
`culature could be imaged. The image acquisition involves
`cooperation of the subject, entails contact with the eye-
`piece, and requires a conscious effort on the part of the
`
`user. All these factors adversely affect the public accept-
`ability of retinal biometric. Retinal vasculature can re-
`veal some medical conditions, e.g., hypertension, which
`is another factor deterring the public accepta