security system using biomatrics

8/7/2019 Security System using Biomatrics

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Security System usingBiometrics

Introduction:

Biometrics refers to the automatic identification of a person based on his/herphysiological or behavioral characteristics. This method of identification is preferred

over traditional methods involving passwords and PIN numbers for various reasons:the person to be identified is required to be physically present at the point-of-

identification; identification based on biometric techniques obviates the need toremember a password or carry a token.

With the increased use of computers as vehicles of information technology, it isnecessary to restrict access to sensitive/personal data. By replacing PINs, biometric

techniques can potentially prevent unauthorized access to or fraudulent use of ATMs,cellular phones, smart cards, desktop PCs, workstations, and computer networks.

PINs and passwords may be forgotten, and token based methods of identification likepassports and driver's licenses may be forged, stolen, or lost. Thus biometric

systems of identification are enjoying a renewed interest. Various types of biometricsystems are being used for real-time identification, the most popular are based on

face recognition and fingerprint matching. However, there are other biometricsystems that utilize iris and retinal scan, speech, facial thermograms, and hand

geometry.A biometric system is essentially a pattern recognition system which makes a

personal identification by determining the authenticity of a specific physiological or

behavioral characteristics possessed by the user. An important issue in designing apractical system is to determine how an individual is identified. Depending on the

context, a biometric system can be

either a verification (authentication) system or an identification system.

Verification vs Identification:There are two different ways to resolve a person's identity: verification andidentification. Verification (Am I whom I claim I am?) involves confirming or denying

a person's claimed identity. In identification, one has to establish a person's identity(Who am I?). Each one of these approaches has it's own complexities and could

probably be solved best by a certain biometric system.Applications:

Biometrics is a rapidly evolving technology which is being widely used in forensicssuch as criminal identification and prison security, and has the potential to be used in

a large range of civilian application areas. Biometrics can be used to preventunauthorized access to ATMs, cellular phones, smart cards, desktop PCs,

workstations, and computer networks. It can be used during transactions conductedvia telephone and internet (electronic commerce and electronic banking). In

automobiles, biometrics can replace keys with key-less entry devices.How They Work:

Although many technologies fit in the biometric space, each works a bit differently.The fingerprint scanners shine a light through a prism that reflects off your finger to

a charge-coupled device (CCD), creating an image that gets processed by an

onboard computer. It's important to note that the actual fingerprint image is notrecorded. Instead, the devices perform a reduction of

the image to data points, called minutiae, that describe the fingerprint layout, calleda template.


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Voice authenticators use a telephone or microphone to record a user's voice pattern,then use that pattern to validate the person. Since these software systems rely on

very low-cost devices, they are generally the least expensive systems to implementfor large numbers of users. The standard caveats learned from voice dictation

systems apply here. These devices must be able to work with background noise andthe variability of off-the-shelf microphones.

Relatively new on the biometric scene, face recognition devices use PC-attachedcameras to record facial geometry. Visionics' FaceIt NT requires an analog camerawith a frame-grabber card that must perform at high speed, while Miros's TrueFace

Network works with any videoconferencing camera.

Once the biometric data is collected, it is encrypted and stored--locally, in the case ofthe desktop-only products; in a central database for the network solutions.

When a user tries to log on, the software compares the incoming biometric dataagainst the stored data.

Some of the major areas of Biometrics that are discussed in this paper areas follows:

l Fingerprint Matchingl Hand Geometry

l Speaker Verification

l Iris Recognition MultiBiometricsll Biometrics-based Web Access

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Fingerprint Matching:

Among all the biometric techniques, fingerprint-based identification is the oldestmethod which has been successfully used in numerous applications. Everyone is


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known to have unique, immutable fingerprints. A fingerprint is made of a series ofridges and furrows on the surface of the finger. The uniqueness of a fingerprint can

be determined by the pattern of ridges and furrows as well as the minutiae points.Minutiae points are local ridge characteristics that occur at either a ridge bifurcation

or a ridge ending.Fingerprint matching techniques can be placed into two categories:minutae-based and correlation based. Minutiae-based techniques first find minutiae

points and then map their relative placement on the finger. However, there are somedifficulties when using this approach. It is difficult to extract the minutiae pointsaccurately when the fingerprint is of low quality. Also this method does not take into

account the global pattern of ridges and furrows. The correlation-based method is

able to overcome some of the difficulties of the minutiae-based approach. However,it has some of its own shortcomings. Correlation-based techniques require the

precise location of a registration point and are affected by image translation androtation.

Fingerprint matching based on minutiae has problems in matching different sized(unregistered) minutiae patterns. Local ridge structures cannot be completely

characterized by minutiae. Efforts are being on to try an alternate representation offingerprints, which will capture more local information and yield a fixed length code

for the fingerprint. The matching will then hopefully become a relatively simple task

of calculating the Euclidean distance will between the two codes.Scientists are developing algorithms which are more robust to noise in fingerprint

images and deliver increased accuracy in real-time. A commercial fingerprint-based

authentication system requires a very low False Reject Rate (FAR) for a given False

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Accept Rate (FAR). This is very difficult to achieve with any one technique. Scientistsare investigating methods to pool evidence

from various matching techniques to increase the overall accuracy of the system. Ina real application, the sensor, the acquisition system and the variation in

performance of the system over time is very critical. Scientists are also field testingthis system on a limited number of users to evaluate the system performance over a

period of time.

Fingerprint Classification:Large volumes of fingerprints are collected and stored everyday in a wide range of

applications including forensics, access control, and driver license registration. Anautomatic recognition of people based on fingerprints requires that the input

fingerprint be matched with a large number of fingerprints in a database (FBIdatabase contains approximately 70 million fingerprints!).


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To reduce the search time and computational complexity, it is desirable to classify

these fingerprints in an accurate and consistent manner so that the input fingerprintis required to be matched only with a subset of the fingerprints in the database.

Fingerprint classification is a technique to assign a fingerprint into one of the severalpre-specified types already established in the literature, which can provide an

indexing mechanism. Fingerprint classification can be viewed as a coarse levelmatching of the fingerprints. An input fingerprint is first matched at a coarse level to

one of the pre-specified types and then, at a finer level, it is compared to the subset

of the database containing that type of fingerprints only. Different algorithms are

developed to classify fingerprints into five classes, namely, whorl, right loop, leftloop, arch, and tented arch. The algorithm separates the number of ridges present in

four directions (0 degree, 45 degree, 90 degree, and 135 degree) by filtering thecentral part of a fingerprint with a bank of Gabor filters. This information is quantized

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to generate a Finger Code which is used for classification. This classification is based

on a two-stage classifier which uses a K-nearest neighbor classifier in the first stage

and a set of neural networks in the second stage. The classifier is tested on 4,000images in the NIST-4 database. For the five-class problem, classification accuracy of

90% is achieved. For the four-class problem (arch and tented archcombined into one class), we are able to achieve a classification accuracy of 94.8%.

By incorporating a reject option, the classification accuracy can be increased to 96%

for the five-class classification and to 97.8% for the four-class classification when

30.8% of the images are rejected.

Fingerprint Image Enhancement:

A critical step in automatic fingerprint matching is to automatically and reliablyextract minutiae from the input fingerprint images. However, the performance of a

minutiae extraction algorithm relies heavily on the quality of the input fingerprintimages.


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In order to ensure that the performance of an automatic fingerprintidentification/verification system will be robust with respect to the quality of the

fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm

in the minutiae extraction module. Scientists have developed a fast fingerprintenhancement algorithm, which can adaptively improve the clarity of ridge and furrow

structures of input fingerprint images based on the estimated local ridge orientation

and frequency. Scientists have evaluated the performance of the imageenhancement algorithm using the goodness index of the extracted minutiae and the

accuracy of an online fingerprint verification system. Experimental results show thatincorporating the enhancement algorithms improves both the goodness index and

the verification accuracy.Hand Geometry:

This approach uses the geometric shape of the hand for authenticating a user'sidentity. Authentication of identity using hand geometry is an interesting problem.

Individual hand features are not descriptive enough for identification. However, it ispossible to devise a method by combining various individual features to attain robust

verification.

Hand Geometry vs Fingerprints:Unlike fingerprints, the human hand isn't unique. One can use finger length,thickness, and curvature for the purposes of verification but not for identification.

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For some kinds of access control like immigration and border control, invasive

biometrics (e.g., fingerprints) may not be desirable as they infringe on privacy. Insuch situations it is desirable to have a biometric system that is sufficient for

verification. As hand geometry is not distinctive, it isthe ideal choice.

Furthermore, hand geometry data is easier to collect. With fingerprint collection goodfrictional skin is required by imaging systems, and with retina-based recognition

systems, special lighting is necessary. Additionally, hand geometry can be easilycombined with other biometrics, namely fingerprint. One can envision a system

where fingerprints are used for (infrequent) identification and hand geometry is usedfor (frequent) verification.

Some of the currently available software performs two basic functions: Capturing Hand Images, and

Extracting Features:The image acquisition system comprises of a light source, a camera, a single mirror

and a flat surface (with five pegs on it). The user places his hand - palm facing

downwards - on the flat surface of the device. The five pegs serve as control pointsfor an appropriate placement of the right hand of the user. The device also has


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knobs to change the intensity of the light source and the focal length of the camera.The lone mirror projects the side-view of the user's hand onto the camera. The

device is hooked to a PC with a GUI application, which provides a live visual feedbackof the top-view and the side-view of the hand. The GUI aids in capturing the hand

image.Feature extraction involves computing the widths and lengths of the fingers at

various locations using the captured image. These metrics define the feature vectorof the user's hand. Currentresearch work involves identifying new features that would result in better

discriminability between two different hands, and designing a deformable model for

the hand.Hand-scan is a relatively accurate technology, but does not draw on as rich a data

set as finger, face, or Voice. A decent measure of the distinctiveness of a biometrictechnology is its ability to perform 1-to-many searches - that is, the ability to

identify a user without the user first claiming an identity. Hand-scan does notperform 1-to-many identification, as similarities between hands are not uncommon.

Where hand-scan does have an advantage is in its FTE (failure to enroll) rates, whichmeasure the likelihood that a user is incapable of enrolling in the system. Finger-

scan, by comparison, is prone to FTE's due to poor quality fingerprints; facial-scan

requires consistent lighting to properly enroll a user. Since nearly all users will havethe dexterity to use hand-scan technology, fewer employees and visitors will need tobe processes outside the biometric.

Speaker VerificationThe speaker-specific characteristics of speech are due to differences in physiological

and behavioral aspects of the speech production system in humans. The mainphysiological aspect of the human speech production system is the vocal tract shape.

The vocal tract is generally considered as the speech production organ above thevocal folds, which consists of the following: (i) laryngeal pharynx (beneath the

epiglottis), (ii) oral pharynx (behind the tongue, between the epiglottis and velum),

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10001111001011 11011000010000 10011010110111 00010111101101 000100010000011 10110111100000 11110000011110 01011110001110 010010100001101 01100001101001 01101011010010 01001100101100 101011000001110 10011011011110 11000000100100 10101001101000 00111101010110 100001110100111 10001111001011 11011000010000 10011010110111 000101111011010 00100010000011 10110111100000 11110000011110 010111100011100 10010100001101 01100001101001 01

10101101001001 00110010110010 10110000011101 00110110111101 100000010010010 10100110100000 11110101011010 00011101001111 000111100101111 01100001000010 01101011011100 01011110110100 010001000001110 11011110000011 11000001111001 01111000111001 00101000011010 110000110100101 10101101001001 00110010110010 10110000011101 001101101111011 00000010010010 10100110100000 11110101011010 000111010011110 00111100101111 01100001000010 01

(iv) nasal pharynx (above the velum, rear end of nasal cavity), and (v) nasal cavity

(above the palate and extending from the pharynx to the nostrils).The vocal tract modifies the spectral content of an acoustic wave as it passes

through it, thereby producing speech. Hence, it is common in speaker verificationsystems to make use of features derived only from the vocal tract. In order to

characterize the features of the vocal tract, the human speech productionmechanism is represented as a discrete-time system of the form depicted in

following diagram:


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The acoustic wave is produced when the airflow from the lungs is carried by thetrachea through the vocal folds. This source of excitation can be characterized as

phonation, whispering, frication, compression, vibration, or a combination of these.Phonated excitation occurs when the airflow is modulated by the vocal folds.

Whispered excitation is produced by airflow rushing through a small triangularopening between the arytenoids cartilage at the rear of the nearly closed vocal folds.

Frication excitation is produced by constrictions in the vocal tract. Compression

excitation results from releasing a completely closed and pressurized vocal tract.Vibration excitation is caused by air being forced through a closure other than the

vocal folds, especially at the tongue. Speech produced by phonated excitation is

called voiced, that produced by phonated excitation plus frication is called mixedvoiced, and that produced by other types of excitation is called unvoiced.

It is possible to represent the vocal-tract in a parametric form as the transferfunction H(z). In order to estimate the parameters of H(z) from the observed speech

waveform, it is necessary to assume some form for H(z). Ideally, the transferfunction should contain poles as well as zeros. However, if only the voiced regions of

11111010101100 00001101000101 11001111000000 11000010011100 111101101001111 00011101001010 01011011110101 11110100100000 111000001011100 00110110001111 10110110101011 00101001111001 110110011100000 00110001011110 01101010010011 10010011000010 10010011111100 001011010111110 11111010101100 00001101000101 11001111000000 110000100111001 11101101001111 00011101001010 01011011110101 111101001000001 11000001011100 00110110001111 10

11011010101100 10100111100111 01100111000000 01100010111100 110101001001110 01001100001010 01001111110000 10110101111101 111101010110000 00110100010111 00111100000011 00001001110011 110110100111100 01110100101001 01101111010111 11010010000011 10000010111000 011011000111110 11011010101100 10100111100111 01100111000000 011000101111001 10101001001110 01001100001010 01001111110000 101101011111011 11101010110000 00110100010111 00

11110000001100 00100111001111 01101001111000 11101001010010 110111101011111 01001000001110 00001011100001 10110001111101 101101010110010 10011110011101 10011100000001 10001011110011 010100100111001 00110000101001 00111111000010 11010111110111 11010101100000 011010001011100 11110000001100 00100111001111 01101001111000 111010010100101 10111101011111 01001000001110 00001011100001 101100011111011 01101010110010 10011110011101 10

01110000000110 00101111001101 01001001110010 01100001010010 011111100001011 01011111011111 01010110000001 10100010111001 111000000110000 10011100111101 10100111100011 10100101001011 011110101111101 00100000111000 00101110000110 11000111110110 11010101100101 001111001110110 01110000000110 00101111001101 01001001110010 011000010100100 11111100001011 01011111011111 01010110000001 101000101110011 11000000110000 10011100111101 10

10011110001110 10010100101101 11101011111010 01000001110000 010111000011011 00011111011011 01010110010100 11110011101100 111000000011000 10111100110101 00100111001001 10000101001001 111110000101101 01111101111101 01011000000110 10001011100111 10000001100001 001110011110110 10011110001110 10010100101101 11101011111010 010000011100000 10111000011011 00011111011011 01010110010100 111100111011001 11000000011000 10111100110101 00

10011100100110 00010100100111 11100001011010 11111011111010 101100000011010 00101110011110 00000110000100 11100111101101 001111000111010 01010010110111 10101111101001 00000111000001 011100001101100 01111101101101 01011001010011 11001110110011 10000000110001 011110011010100 10011100100110 00010100100111 11100001011010 111110111110101 01100000011010 00101110011110 00000110000100 111001111011010 01111000111010 01010010110111 10

10111110100100 00011100000101 11000011011000 11111011011010 101100101001111 00111011001110 00000011000101 11100110101001 001110010011000 01010010011111 10000101101011 11101111101010 110000001101000 10111001111000 00011000010011 10011110110100 11110001110100 101001011011110 10111110100100 00011100000101 11000011011000 111110110110101 01100101001111 00111011001110 00000011000101 111001101010010 01110010011000 01010010011111 10

00000111100000 11110010111100 01110010010100 00110101100001 101001011010110 10010010011001 01100101011000 00111010011011 011110110000001 00100101010011 01000001111010 10110100001110 100111100011110 01011110110000 10000100110101 10111000101111 01101000100010 000011101101111 00000111100000 11110010111100 01110010010100 001101011000011 01001011010110 10010010011001 01100101011000 001110100110110 11110110000001 00100101010011 01

00000111101010 11010000111010 01111000111100 10111101100001 000010011010110 11100010111101 10100010001000 00111011011110 000011110000011 11001011110001 11001001010000 11010110000110 100101101011010 01001001100101 10010101100000 11101001101101 11101100000010 010010101001101 00000111101010 11010000111010 01111000111100 101111011000010 00010011010110 11100010111101 10100010001000 001110110111100 00011110000011 11001011110001 11

00100101000011 01011000011010 01011010110100 10010011001011 001010110000011 10100110110111 10110000001001 00101010011010 000011110101011 01000011101001 11100011110010 11110110000100 001001101011011 10001011110110 10001000100000 11101101111000 00111100000111 100101111000111 00100101000011 01011000011010 01011010110100 100100110010110 01010110000011 10100110110111 10110000001001 001010100110100 00011110101011 01000011101001 11

10001111001011 11011000010000 10011010110111 00010111101101 000100010000011 10110111100000 11110000011110 01011110001110 010010100001101 01100001101001 01101011010010 01001100101100 101011000001110 10011011011110 11000000100100 10101001101000 00111101010110 100001110100111 10001111001011 11011000010000 10011010110111 000101111011010 00100010000011 10110111100000 11110000011110 010111100011100 10010100001101 01100001101001 01

10101101001001 00110010110010 10110000011101 00110110111101 100000010010010 10100110100000 11110101011010 00011101001111 000111100101111 01100001000010 01101011011100 01011110110100 010001000001110 11011110000011 11000001111001 01111000111001 00101000011010 110000110100101 10101101001001 00110010110010 10110000011101 001101101111011 00000010010010 10100110100000 11110101011010 000111010011110 00111100101111 01100001000010 01

speech are used then an all-pole model for H(z) is sufficient. Furthermore, linearprediction analysis can be used to efficiently estimate the parameters of an all-pole

model. Finally, it can also be noted that the all-pole model is the minimum-phasepart of the true model and has an identical magnitude spectra, which contains the

bulk of the speaker-dependent information.

The above discussion also underlines the text-dependent nature of the vocal-tractmodels. Since the model is derived from the observed speech, it is dependent on the

speech. Following Figure illustrates the differences in the models for two speakerssaying the same vowel.


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Speaker Modeling :

Utterances spoken by the same person but at different times result in similar yet adifferent sequence of feature vectors. The purpose of voice modeling is to build a

model that captures these variations in the extracted set of features. There are two

types of models that have been used extensively in speaker verification and speechrecognition systems: stochastic models and template models. The stochastic modeltreats the speech production process as a parametric random process and assumes

that the parameters of the underlying stochastic process can be estimated in aprecise, well-defined manner. The template model attempts to model the speech

production process in a non-parametric manner by retaining a number of sequencesof feature vectors derived from multiple utterances of the same word by the same

person. Template models dominated early work in speaker verification and speech11111010101100 00001101000101 11001111000000 11000010011100 111101101001111 00011101001010 01011011110101 11110100100000 111000001011100 00110110001111 10110110101011 00101001111001 110110011100000 00110001011110 01101010010011 10010011000010 10010011111100 001011010111110 11111010101100 00001101000101 11001111000000 110000100111001 11101101001111 00011101001010 01011011110101 111101001000001 11000001011100 00110110001111 10

11011010101100 10100111100111 01100111000000 01100010111100 110101001001110 01001100001010 01001111110000 10110101111101 111101010110000 00110100010111 00111100000011 00001001110011 110110100111100 01110100101001 01101111010111 11010010000011 10000010111000 011011000111110 11011010101100 10100111100111 01100111000000 011000101111001 10101001001110 01001100001010 01001111110000 101101011111011 11101010110000 00110100010111 00

11101101000100 010000011101 1011110000011110 00001111001011 110001110010010 10000110101100 00110100101101 01101001001001 100101100101011 000001110100110110111 101100000010010010101 001101000001111 01010110100001 11010011110001 1110010 11110110000100001 00110101101110 00101111011010 0010001000001110110111 10000011110000 01111001011110 00111001001010 00011010110000 110100101101011 01001001001100 10110010101100 00011101001101

10111101100000 01001001010100 11010000011110 10101101000011 101001111000111 10010111101100 00100001001101 01101110001011 110110100010001 00000111011011 11000001111000 00111100101111 000111001001010 00011010110000 11010010110101 10100100100110 01011001010110 000011101001101 10111101100000 01001001010100 11010000011110 101011010000111 01001111000111 10010111101100 00100001001101 011011100010111 10110100010001 00000111011011 11

00000111100000 11110010111100 01110010010100 00110101100001 101001011010110 10010010011001 01100101011000 00111010011011 011110110000001 00100101010011 01000001111010 10110100001110 100111100011110 01011110110000 10000100110101 10111000101111 01101000100010 000011101101111 00000111100000 11110010111100 01110010010100 001101011000011 01001011010110 10010010011001 01100101011000 001110100110110 11110110000001 00100101010011 01

00000111101010 11010000111010 01111000111100 10111101100001 000010011010110 11100010111101 10100010001000 00111011011110 000011110000011 11001011110001 11001001010000 11010110000110 100101101011010 01001001100101 10010101100000 11101001101101 11101100000010 010010101001101 00000111101010 11010000111010 01111000111100 101111011000010 00010011010110 11100010111101 10100010001000 001110110111100 00011110000011 11001011110001 11

00100101000011 01011000011010 01011010110100 10010011001011 001010110000011 10100110110111 10110000001001 00101010011010 000011110101011 01000011101001 11100011110010 11110110000100 001001101011011 10001011110110 10001000100000 11101101111000 00111100000111 100101111000111 00100101000011 01011000011010 01011010110100 100100110010110 01010110000011 10100110110111 10110000001001 001010100110100 00011110101011 01000011101001 11

10001111001011 11011000010000 10011010110111 00010111101101 000100010000011 10110111100000 11110000011110 01011110001110 010010100001101 01100001101001 01101011010010 01001100101100 101011000001110 10011011011110 11000000100100 10101001101000 00111101010110 100001110100111 10001111001011 11011000010000 10011010110111 000101111011010 00100010000011 10110111100000 11110000011110 010111100011100 10010100001101 01100001101001 01

10101101001001 00110010110010 10110000011101 00110110111101 100000010010010 10100110100000 11110101011010 00011101001111 000111100101111 01100001000010 01101011011100 01011110110100 010001000001110 11011110000011 11000001111001 01111000111001 00101000011010 110000110100101 10101101001001 00110010110010 10110000011101 001101101111011 00000010010010 10100110100000 11110101011010 000111010011110 00111100101111 01100001000010 01

recognition because the template model is intuitively more reasonable. However,recent work in stochastic models has demonstrated that these models are more

flexible and hence allow for better modeling of the speech production process. A very

popular stochastic model for modeling the speech production process is the HiddenMarkov Model (HMM). HMMs are extensions to the conventional Markov models,wherein the observations are a probabilistic function of the state, i.e., the model is a

doubly embedded stochastic process where the underlying stochastic process is not

directly observable (it is hidden). The HMM can only be viewed through another setof stochastic processes that produce the sequence of observations. Thus, the HMM is

a finite-state machine, where a probability density function p(x | s_i) is associatedwith each state s_i. The states are connected by a transition network, where the


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state transition probabilities are a_{ij} = p(s_i | s_i). A fully connected three-stateHMM is depicted in figure 4.

For speech signals, another type of HMM, called a left-right model or a Bakis model,is found to be more useful. A left-right model has the property that as time

increases, the state index increases (or stays the same)-- that is the system statesproceed from left to right. Since the properties of a speech signal change over time

in a successive manner, this model is very well suited for modeling the speechproduction process.

Pattern Matching :The pattern matching process involves the comparison of a given set of input feature

vectors against the speaker model for the claimed identity and computing amatching score. For the

Hidden Markov models discussed above, the matching score is the probability that a

given set of feature vectors was generated by the model.

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10101101001001 00110010110010 10110000011101 00110110111101 100000010010010 10100110100000 11110101011010 00011101001111 000111100101111 01100001000010 01101011011100 01011110110100 010001000001110 11011110000011 11000001111001 01111000111001 00101000011010 110000110100101 10101101001001 00110010110010 10110000011101 001101101111011 00000010010010 10100110100000 11110101011010 000111010011110 00111100101111 01100001000010 01

A Speaker Verification System:


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Iris Recognition

Iris recognition is a new field of Pattern recognition. Iris recognition is based onvisible (via regular and/or infrared light) qualities of the iris.

11111010101100 00001101000101 11001111000000 11000010011100 111101101001111 00011101001010 01011011110101 11110100100000 111000001011100 00110110001111 10110110101011 00101001111001 110110011100000 00110001011110 01101010010011 10010011000010 10010011111100 001011010111110 11111010101100 00001101000101 11001111000000 110000100111001 11101101001111 00011101001010 01011011110101 111101001000001 11000001011100 00110110001111 10

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11110000001100 00100111001111 01101001111000 11101001010010 110111101011111 01001000001110 00001011100001 10110001111101 101101010110010 10011110011101 10011100000001 10001011110011 010100100111001 00110000101001 00111111000010 11010111110111 11010101100000 011010001011100 11110000001100 00100111001111 01101001111000 111010010100101 10111101011111 01001000001110 00001011100001 101100011111011 01101010110010 10011110011101 10

A primary visible characteristic is the trabecular meshwork (permanently formed bythe 8th month of gestation), a tissue which gives the appearance of dividing the iris

in a radial fashion. Other visible characteristics include rings, furrows, freckles, and


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the corona, to cite only the more familiar. Expressed simply, iris recognitiontechnology converts these visible characteristics into a 512 byte IrisCode(tm), a

template stored for future verification attempts. 512 bytes is a fairly compact size fora biometric template, but the quantity of information derived from the iris is

massive. The density of information is such that each iris can be said to have 266unique "spots", as opposed to 13-60 for traditional biometric technologies. This '266'

measurement is cited in all iris recognition literature.; after allowing for thealgorithm's correlative functions and for characteristics inherent to most humaneyes., It has been concluded that 173 "independent binary degrees-of-freedom" can

be extracted from his algorithm - an exceptionally large number for a biometric

MultiBiometrics

Integrating Faces and Fingerprints for Personal Identification :

An automatic personal identification system based solely on fingerprints or faces isoften not able to meet the system performance requirements. Face recognition is

fast but not reliable while fingerprint verification is reliable but inefficient in databaseretrieval. Biometric systems are being developed which integrate faces and

fingerprints. The system overcomes the limitations of face recognition systems as

well as fingerprint verification systems. The integrated prototype system operates inthe identification mode with an admissible response time. The identity established bythe system is more reliable than the identity established by a face recognition

system. In addition, the proposed decision fusion schema enables performanceimprovement by integrating multiple cues with different confidence measures.

Experimental results demonstrate that this system performs very well. It meets theresponse time as well as the accuracy requirements.

A Multimodal Biometric System Using Fingerprint, Face, and Speech:A biometric system which relies only on a single biometric identifier in making a

personal identification is often not able to meet the desired performancerequirements. Identification based on multiple biometrics represents an emerging

trend. This problem is solved by a multimodal biometric system, which integrates

face recognition, fingerprint verification, and speaker verification in making apersonal identification. This system takes advantage of the capabilities of each

individual biometric. It can be used to overcome some of the limitations of a singlebiometrics. Preliminary experimental results demonstrate that the identity

established by such an integrated system is more reliable than the identityestablished by a face recognition system, a fingerprint verification system, and a

speaker verification system.

Biometrics-based Web Access

Authentication and encryption are crucial to network security. Public keycryptography provides a secure way to exchange information but designing a high

security authentication system still remains an open problem. Complex passwordsare easy to forget while simple passwords are easily guessed by unauthorized

persons. Several of the biometric characteristics of an individual are unique and do00100101000011 01011000011010 01011010110100 10010011001011 001010110000011 10100110110111 10110000001001 00101010011010 000011110101011 01000011101001 11100011110010 11110110000100 001001101011011 10001011110110 10001000100000 11101101111000 00111100000111 100101111000111 00100101000011 01011000011010 01011010110100 100100110010110 01010110000011 10100110110111 10110000001001 001010100110100 00011110101011 01000011101001 11

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not change over time. These properties make biometrics well suited for

authentication. Authentication systems based on fingerprints, voice, iris, and handgeometry exist for applications such as passport control, forensics, automatic teller

machines, driver license, and border control. With the increasing growth of theInternet, there is a need to restrict access to sensitive data on the Web to authorized

users. We have developed a prototype system which uses hand geometry toauthenticate users to restrict access to web pages. Initial evaluation of the prototype


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system is encouraging. Similar techniques can be used to authenticate people for e-commerce applications

The Flow Diagram:

security system using biomatrics

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