Efficient Methods of Multimodal Biometric

Security System-

Fingerprint Authentication,

Speech and Face Recognition

SUBMITTED BY:

MAYANK PAL

ABSTRACT

This presentation proposes the efficient methods in multimodal biometric i.e. fingerprint,

speech, face.

Multimodal system is developed through fusion of fingerprint, speech and face recognition.

The proposed system is designed for applications where the training database contains a face,

fingerprint images and voice data .

The multimodal biometric security system may be used in various application areas such as, for

authentication number of employees working in offices, in military applications and also in all

possible security applications.

CONTENT

I. Introduction of Biometrics

II. Aim

III. Methodology

IV. Multimodal Biometric Systems

V. Biometric Techniques

VI. Modules in Multimodal Biometric Systems

VII. Levels of Fusion

VIII.Result

IX. Conclusion

X. Bibliography

INTRODUCTION

The term biometric is usually associated with the use of unique physiological characteristics to

identify an individual. One of the applications which most people associate with biometrics is

security.

It is an automated method of recognizing a person based on a physiological or behavioral

characteristic such as face ,fingerprints, hand geometry, handwriting, iris, voice etc.

As password or PIN can lost or forgotten, biometrics cannot be forgotten or lost and requires

physical presence of the person to be authenticated.

Thus personal authentication systems using biometrics are more reliable, convenient and

efficient than the traditional identification methods.

However, even the best biometric traits till date are facing numerous problems biometric

authentication systems generally suffer from enrolment problems due to non-universal

biometric traits, susceptibility to biometric spoofing or insufficient accuracy caused by noisy

data acquisition in certain environments.

Thus to overcome these problems ,multimodal biometric system can be used to reduce/remove

the limitations of unimodal systems.

AIM

As the level of security breaches and transaction fraud increases, the need for highly secure

identification and personal verification technologies is becoming apparent.

In recent years, biometrics authentication has seen considerable improvements in reliability

and accuracy, with some of the traits offering good performance.

The reason to combine different modalities is to improve recognition rate.

The aim of multimodal biometrics is to reduce one or more of the following:

FAR(False Acceptance Rate) : It is a measure of the percent of invalid inputs that are

incorrectly accepted.

FRR(False Reject Rate) : It is a measure of the percent of valid inputs that are incorrectly

rejected.

CER(Crossover Error Rate) : The rate at which both the accept and reject errors are equal.

- a lower value of the CER is more accurate for Biometric System.

MULTIMODAL BIOMETRIC SYSTEMS

Multimodal biometric systems are those that utilize more than one physiological or behavioural

characteristic for enrolment , verification, or identification.

In applications such as border entry/exit, access control, civil identification, and network

security, multi-modal biometric systems are looked to as a means of reducing false non-match

and false match rates, providing a secondary means of enrolment , verification, and

identification.

A multi biometric system uses multiple sensors for data acquisition. This allows capturing

multiple samples of a single biometric trait (called multi-sample biometrics) and/or samples of

multiple biometric traits (called multi source or multimodal biometrics).

A unimodal biometric system consists of three major modules: sensor module, feature

extraction module and matching module. The performance of a biometric system is largely

affected by the reliability of the sensor used and the degrees of freedom offered by the features

extracted from the sensed signal.

Further, if the biometric trait being sensed or measured is noisy (a fingerprint with a scar or a

voice altered by a cold, for example), the resultant matching score computed by the matching

module may not be reliable. This problem can be solved by installing multiple sensors that

capture different biometric traits. Such systems, known as multimodal biometric systems , are

expected to be more reliable due to the presence of multiple pieces of evidence.

BIOMETRIC TECHNIQUES

FINGERPRINT TECHNOLOGY: It is the oldest and most widely used method.

It needs a fingerprint reader.

Registered points are located and compared.

Optical sensors are used for scanning purpose.

It can be used for many applications like pc login security, voting system, attendance system

etc.

Uses the ridge endings and bifurcation's on a persons finger to plot points known as Minutiae

The number and locations of the minutiae vary from finger to finger in any particular person,

and from person to person for any particular finger

Finger

ImageFinger Image + Minutiae Minutiae

FACE RECOGNITION TECHNOLOGY: Face Recognition is a biometric technique for automatic identification or verification of a

person from a digital image.

These include the position/size/shape of the eyes, nose, cheekbones and jaw line.

SPEECH RECOGNITION TECHNOLOGY:

It is a biometric process of validating a user's claimed identity using characteristics

extracted from their voices.

It uses the pitch, pattern, tone, frequency, rhythm of speech for identification purposes.

During the enrollment phase, the spoken words are converted from analog to digital

format, and the distinctive vocal characteristics such as pitch, frequency, and tone, are

extracted, and a speaker model is established.

A template is then generated and stored for future comparisons.

Comparison Between Different Technique

MODULES IN MULTIMODEL BIOMETRIC SYSTEM

A common biometric system mainly involves the following major modules-

1. Sensor Module At sensor module a suitable user interface incorporating the biometric sensor or scanner is needed

to measure or record the raw biometric data of the user. This raw biometric data is captured and

then it is transferred to the next module for feature extraction. The design of the sensor module

influences the various factors like cost and size.

2. Feature Extraction ModuleAt feature extraction module the quality of the acquired biometric data from the sensor is assessed

initially for further processing. Thus generating a synoptic but indicative digital representation of

the underlying traits or modalities. After extracting the features it is given as input to the matching

module for further comparison.

3. Matching ModuleThe extracted features when compared with the templates in the database generate a match score.

This match score may be controlled by the quality of the given biometric data. The matching

module also condensed a decision making module in which the generated match score is used to

validate the claimed identity.

4. Decision making moduleDecision making module identifies whether the user is a genuine user or an impostor based on the

match scores. These are used to either validate the identity of a person or provides a ranking of the

enrolled identities for identifying an individual.

A simple block diagram for multi-modal biometric system is shown in Fig

MODE OF OPERATION The two major mode of operation in multi-modal biometric systems are

Serial mode

Parallel mode

In serial mode of operation, multiple sources of information is not acquired simultaneously,

that is the user goes through stage by stage authentication process.

Thus the recognition time is improved in serial mode as decision is made before getting all the

traits.

In case of parallel mode of operation, recognition is performed by acquiring multiple sources of

information simultaneously.

This will reduce the efficiency of the system and in turn cause inconvenience to the user.

Study reveals that combined use of both modes may result a system which provides high

efficiency and user convenience.

..

.

LEVELS OF FUSION

By employing the information available in any of the modules like sensor level, feature

extraction level, matching level and Decision making level, fusion can be developed in multi-

modal biometric system like sensor level fusion, feature level fusion, matching score level

fusion and decision level fusion.

The different biometric identifier used in the multimodal biometric system, their information

from the individual identifier is taken together and can be fused at different levels of fusion

such as fusion at sensor level , fusion at feature level, fusion at matching score level and the

fusion at decision level

The following figure shows the fusion at Sensor level which involves combining raw data from

various sensors and this fusion can be appropriate for multi-sample and multi-sensor systems

Fig. Sensor level

fusion

Feature level fusion shown in Fig refers to combining the different feature sets extracted from

multiple biometric modalities into a single feature vector.

Fig. Feature level fusion

Matching score level fusion shown in Fig refers to the combination of similarity scores

provided by a matching module for each input features and template biometric feature

vectors in the database.

Fig. Matching score level fusion

In decision level fusion as shown in Fig, the information integration occurs when each

biometric system makes an independent decision about the identity of the user or verifies the

claimed identity.

Fig: Decision level fusion

RESULT

A multimodal biometrics system helps us to reduce:

• False accept rate (FAR)

• False reject rate (FRR)

• Failure to enrol rate (FTE)

It also increases:

• Sensor cost

• Enrolment time

• Transit times

• Need for a prior knowledge/data

• System development and complexity

CONCLUSION

Though there are many multi-modal biometric systems in practice for authentication of a

person, selection of appropriate modal, choice of optimal fusion level and redundancy in the

extracted features are still some of the shortcomings faced in the design of multi-modal

biometric system that needs to be addressed.

The different approaches that are possible in multi-modal biometric systems, the suitable

fusion levels, and the integration strategies that can be chosen to consolidate information

were discussed.

The combination of more than one biometrics can apply to enhance the security.

Performance and the advanced security level made the multi-modal biometric systems

popular in these days and has great scope in future.

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Proceedings of Auto ID 99, pp. 59-64, 1999.

2. A. Ross & A. K. Jain, Information Fusion in Biometrics, Pattern Recognition

Letters, 24 (13), pp. 2115-2125, 2003.

3. A.S. & A. A. Raza , Combined Classifier for Invariant Face Recognition,

Pattern Analysis and Applications, 3(4), pp. 289-302, 2000

4. [A. Ross, A. K. Jain & J.A. Riesman, Hybrid fingerprint matcher, Pattern

Recognition, 36, pp. 1661–1673, 2003

5. W. T. Tan & A. K. Jai , Combining Face and Iris Biometrics for Identity

Verification, Proceedings of Fourth International Conference on AVBPA, pp.

805-813, 2003

REFERENCES

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