fusion by biometrics 主講人：李佳明、陳明暘指導教授：林維暘. outline...

Fusion by BiometricsFusion by Biometrics

主講人：李佳明、陳明暘主講人：李佳明、陳明暘指導教授：林維暘指導教授：林維暘

OutlineOutline

IntroductionIntroduction Biometric system Biometric system Feature extractionFeature extraction The advantage of verification in biometricsThe advantage of verification in biometrics The flow of verification The flow of verification Fusion methodsFusion methods Experiment ResultsExperiment Results ConclusionConclusion ReferenceReference

IntroductionIntroduction

Multimodal biometrics systems consolidate Multimodal biometrics systems consolidate the evidence presented by multiple the evidence presented by multiple biometric sources and typically provide biometric sources and typically provide better recognition performance compared better recognition performance compared to systems based on a single biometric to systems based on a single biometric modality.modality.

Multi-biometrics systems provide anti-Multi-biometrics systems provide anti-spoofing measures by making it difficult for spoofing measures by making it difficult for an intruder to spoof multiple biometric an intruder to spoof multiple biometric traits simultaneously.traits simultaneously.

BiometricsBiometrics

Comparison of various biometric technologies

(H=High, M=Medium, L=Low)

Biometrics Universality Uniqueness Permanence Collectability Performance Acceptability Circumvention

Face H L M H L H L

Fingerprint M H H M H M H

Hand geometry M M M H M M M

Keystrokes L L L M L M M

Hand veins M M M M M M H

Iris H H H M H L H

Retinal scan H H M L H L H

Signature L L L H L H L

Voice M L L M L H L

facial thermogram

H H L H M H H

Odor H H H L L M L

DNA H H H L H L L

Gait M L L H L H M

Ear recognition M M H M M H M

The advantage of Multimodal The advantage of Multimodal BiometricBiometric

Multiple biometric sources enhance matching performance.

Reducing failure to enroll rate.

Difficult to spoof multiple traits simultaneously.

A biometric systemA biometric system

http://upload.wikimedia.org/wikipedia/commons/3/3c/Biometric_system_diagram.png


A biometric-based authentication system A biometric-based authentication system operates in two modesoperates in two modes

1. Enrollment mode1. Enrollment mode

2. Authentication mode2. Authentication mode


1. Enrollment1. Enrollment ：：

A user’s biometric data is acquired using a A user’s biometric data is acquired using a biometric reader and stored in a database.biometric reader and stored in a database.

The stored template is labeled with a user The stored template is labeled with a user identity (e.g., name, identification number, identity (e.g., name, identification number, etc.) to facilitate authentication.etc.) to facilitate authentication.


2. Authentication2. Authentication ：：

A user’s biometric data is once again acquired A user’s biometric data is once again acquired and the system uses this to either identify and the system uses this to either identify who the user is, or verify the claimed identity who the user is, or verify the claimed identity of the user.of the user.

IdentificationIdentification ：： Comparing the acquired biometric Comparing the acquired biometric information against templates corresponding to all information against templates corresponding to all users in the database.users in the database.

VerificationVerification ：： Comparison with only those templates Comparison with only those templates corresponding to the claimed identity.corresponding to the claimed identity.

Feature extractionFeature extraction

FingerprintFingerprint FaceFace Hand GeometryHand Geometry IrisIris


Fingerprint friction ridge details are Fingerprint friction ridge details are generally described in a hierarchical generally described in a hierarchical order at three different levelsorder at three different levels ：： Level 1 - patternLevel 1 - pattern Level 2 - minutia pointsLevel 2 - minutia points Level 3 - pores and ridge contoursLevel 3 - pores and ridge contours

Automated Fingerprint Identification Automated Fingerprint Identification Systems (AFIS) currently rely only on Systems (AFIS) currently rely only on Level 1 and Level 2 features.Level 1 and Level 2 features.


Level 1 features, or patterns, are the Level 1 features, or patterns, are the macro details of the fingerprint such as macro details of the fingerprint such as ridge flow and pattern type.ridge flow and pattern type.


Level 2 features, or points, refer to the Level 2 features, or points, refer to the Galton characteristics or minutiae, such Galton characteristics or minutiae, such as ridge bifurcations and endings.as ridge bifurcations and endings.


Level 3 features, or shape, include all Level 3 features, or shape, include all dimensional attributes of the ridge such dimensional attributes of the ridge such as ridge path deviation, width, shape, as ridge path deviation, width, shape, pores, edge contour, incipient ridges, pores, edge contour, incipient ridges, breaks, creases, scars, and other breaks, creases, scars, and other permanent details.permanent details.


Fingerprint image resolution. The same Fingerprint image resolution. The same fingerprint captured at three different fingerprint captured at three different image resolutions image resolutions (a) 380 ppi (Identix 200DFR)(a) 380 ppi (Identix 200DFR) (b) 500 ppi (CrossMatch ID1000)(b) 500 ppi (CrossMatch ID1000) (c) 1,000 ppi (CrossMatch ID1000).(c) 1,000 ppi (CrossMatch ID1000).


Different levels of fingerprint features Different levels of fingerprint features detected.detected.

Level 3 features are matched using the Level 3 features are matched using the ICP algorithm.ICP algorithm.


Reference point (Reference point (XX), the region of ), the region of interest, and 80 sectors (interest, and 80 sectors (B B = 5, = 5, k k = 16) = 16) superimposed on a fingerprintsuperimposed on a fingerprint


Face recognition is an important Face recognition is an important biometric identification technology. biometric identification technology. Facial scan is an effective biometric Facial scan is an effective biometric attribute/indicator.attribute/indicator.

The performance of face recognition The performance of face recognition systems dependent on consistent systems dependent on consistent conditions such as lighting, pose and conditions such as lighting, pose and facial expression.facial expression.


Preprocessing 幾何位置的調整 - 取人臉影像中兩個 control point ，

分別為左眼的中心點和右眼的中心點，利用這兩個控制點。

明亮度的調整 - histogram equalization ，此步驟是為了縮小各張影像之間亮度的改變所造成的差異


擷取三個人臉區域，在每個區域裡面，全部的影像灰階值都會被儲存在一個向量，該向量就是該區域的特徵向量。

利用了 Principal Component Analysis (PCA) 將特徵向量降維。


Automatic feature extraction for 3D Automatic feature extraction for 3D face matching.face matching.


Segmentation of facial scan.Segmentation of facial scan.


For a frontal facial scan, nose tip usually For a frontal facial scan, nose tip usually has the largest z value.has the largest z value.


Pose angle quantization.Pose angle quantization.

Example of directional maximum.Example of directional maximum.


extracted nose profiles.extracted nose profiles.


Feature extraction results which Feature extraction results which lead to correct 3D face matches.lead to correct 3D face matches.


Some biometrics may not be acceptable Some biometrics may not be acceptable for the sake of protecting an individual's for the sake of protecting an individual's privacy.privacy.

As hand geometry information is not As hand geometry information is not very distinctive, it is distinctive enough very distinctive, it is distinctive enough for verification but not for identification.for verification but not for identification.

It is simple method of sensing which It is simple method of sensing which does not impose undue requirements on does not impose undue requirements on the imaging optics.the imaging optics.


Hand geometry sensing device.Hand geometry sensing device. 5 images of the same hand are taken.5 images of the same hand are taken.


Hand shape alignmentHand shape alignment We represent the shape of a hand by a We represent the shape of a hand by a

set of ordered points in the Euclidean set of ordered points in the Euclidean plane.plane.


The fourteen axes along which feature The fourteen axes along which feature values are computed.values are computed.


The iris is a protected internal organ The iris is a protected internal organ whose random texture is complex, whose random texture is complex, unique, and stable throughout life .unique, and stable throughout life .

It can serve as a kind of living passport It can serve as a kind of living passport or password that one need not or password that one need not remember but can always present.remember but can always present.

"Biometric Personal Identification System "Biometric Personal Identification System Based on Iris Analysis."Based on Iris Analysis." U.S. Patent No. U.S. Patent No. 5,291,560 issued March 1, 1994 (J. 5,291,560 issued March 1, 1994 (J. Daugman). Daugman).


Finding an Iris in an ImageFinding an Iris in an Image minimum of 70 pixels in iris radius. Iris radius minimum of 70 pixels in iris radius. Iris radius

of 80 to 130 pixels has been more typical.of 80 to 130 pixels has been more typical. Monochrome CCD cameras (480 x 640) have Monochrome CCD cameras (480 x 640) have

been used.been used. using a coarse-to-fine strategy terminating in using a coarse-to-fine strategy terminating in

single-pixel precision estimates of the center single-pixel precision estimates of the center coordinates and radius of both the iris and the coordinates and radius of both the iris and the pupil.pupil.


The outline overlay shows results of the The outline overlay shows results of the iris and pupil localization.iris and pupil localization.


Iris Feature Encoding by 2D Wavelet Iris Feature Encoding by 2D Wavelet Demodulation.Demodulation. Each isolated iris pattern is then demodulated Each isolated iris pattern is then demodulated

to extract its phase information using to extract its phase information using quadrature 2D Gabor wavelets.quadrature 2D Gabor wavelets.

This process is repeated all across the iris This process is repeated all across the iris with many wavelet sizes, frequencies, and with many wavelet sizes, frequencies, and orientations, to extract 2,048 bits.orientations, to extract 2,048 bits.


Advantages of the Iris for Advantages of the Iris for IdentificationIdentification Highly protected, internal organ of the eye.Highly protected, internal organ of the eye. Externally visible; Externally visible; high degree of randomness .high degree of randomness . Pre-natal morphogenesis (7th month of Pre-natal morphogenesis (7th month of

gestation) gestation) Limited genetic penetrance of iris patterns Limited genetic penetrance of iris patterns Patterns apparently stable throughout life Patterns apparently stable throughout life Encoding and decision-making are tractable Encoding and decision-making are tractable


Disadvantages of the Iris for Disadvantages of the Iris for IdentificationIdentification Small target to acquire from a distance Small target to acquire from a distance Located behind a curved, wet, reflecting Located behind a curved, wet, reflecting

surface surface Obscured by eyelashes, lenses, reflections Obscured by eyelashes, lenses, reflections Partially occluded by eyelids, often drooping Partially occluded by eyelids, often drooping Deforms non-elastically as pupil changes size Deforms non-elastically as pupil changes size Illumination should not be visible or bright Illumination should not be visible or bright

A biometric system has four important A biometric system has four important componentscomponents

1. Sensor module1. Sensor module : : Acquire the biometric data of an individual.Acquire the biometric data of an individual.

2. Feature extraction module : 2. Feature extraction module : Acquire data is processed to extract feature Acquire data is processed to extract feature

values.values.

A biometric system has four important A biometric system has four important componentscomponents

3.3. Matching module : Matching module : Feature values are compared against those in Feature values are compared against those in

the template by generating a matching score.the template by generating a matching score.

4.4. Decision-making module :Decision-making module : The user’s identity is established or a claimed The user’s identity is established or a claimed

identity is either accepted or rejected based on identity is either accepted or rejected based on the matching score generated in the matching the matching score generated in the matching module.module.

Fusion in biometricsFusion in biometrics

(1) Fusion at the (1) Fusion at the feature extractionfeature extraction level : level :

1. The data obtained from each sensor is used to1. The data obtained from each sensor is used to

compute a feature vector.compute a feature vector.

2. Concatenate the two vectors into a single new 2. Concatenate the two vectors into a single new

vector.vector.

3. Feature reduction techniques may be 3. Feature reduction techniques may be employed.employed.

Multimodal biometric system

A prototype multimodal biometric system.


(2) Fusion at the (2) Fusion at the matching scorematching score level : level :

Each system provides a matching score Each system provides a matching score indicating the proximity of the feature vector indicating the proximity of the feature vector with the template vector.with the template vector.

These scores can be combined to assert the These scores can be combined to assert the veracity of the claimed identity.veracity of the claimed identity.


(3) Fusion at the (3) Fusion at the decisiondecision level: level:

Each sensor can capture multiple biometric Each sensor can capture multiple biometric data and the resulting feature vectors data and the resulting feature vectors individually classified into the two classes –– individually classified into the two classes –– accept or reject.accept or reject.

A majority vote scheme can be used to make A majority vote scheme can be used to make the final decision. the final decision.


Fusion in the context of biometrics can Fusion in the context of biometrics can take the following forms :take the following forms :

(1) Single biometric multiple representation.(1) Single biometric multiple representation.

(2) Single biometric multiple matchers.(2) Single biometric multiple matchers.

(3) Multiple biometric fusion.(3) Multiple biometric fusion.


(1) Single biometric multiple (1) Single biometric multiple representation.representation.

This type of fusion involves using multiple This type of fusion involves using multiple representations on a single biometric representations on a single biometric indicator.indicator.

Typically, each representation has its own Typically, each representation has its own classifier.classifier.


(2) Single biometric multiple matchers.(2) Single biometric multiple matchers.

It is also possible to incorporate multiple It is also possible to incorporate multiple matching strategies in the matching module matching strategies in the matching module of a biometric system and combine the scores of a biometric system and combine the scores generated by these strategies.generated by these strategies.


(3) Multiple biometric fusion.(3) Multiple biometric fusion.

By integrating matching scores obtained from By integrating matching scores obtained from multiple biometric sources.multiple biometric sources.

These include majority voting, sum and These include majority voting, sum and product rules, k-NN classifiers, SVMs, decision product rules, k-NN classifiers, SVMs, decision trees, Bayesian methods, etc.trees, Bayesian methods, etc.


(4) Others(4) Others

1. Store multiple templates in database.1. Store multiple templates in database.

Example : A fingerprint biometric system may Example : A fingerprint biometric system may store multiple templates of a users fingerprint store multiple templates of a users fingerprint (same finger) in its database. When a (same finger) in its database. When a fingerprint impression is presented to the fingerprint impression is presented to the system for verification, it is compared against system for verification, it is compared against each of the templates, and the matching each of the templates, and the matching score generated by these multiple matchings score generated by these multiple matchings are integrated.are integrated.


(4) Others(4) Others

2. A system may store a single template of a 2. A system may store a single template of a usersusers

finger, but acquire multiple impressions of finger, but acquire multiple impressions of thethe

finger during verification.finger during verification.

3. Another possibility would be to acquire and 3. Another possibility would be to acquire and useuse

impressions of multiple fingers for every impressions of multiple fingers for every user.user.

Experiment ResultsExperiment Results

50 users50 users Biometrics : Biometrics : fingerprint , face , hand

geometry Sum rule

Experiment ResultsExperiment Results

ConclusionConclusion

Multimodal biometric systems provide Multimodal biometric systems provide better recognition performance.better recognition performance.

Different users tend to adopt differently to individual biometric indicators. These weights can be learnt over time by examining the stored template of the user.

ReferenceReference

1.1. http://en.wikipedia.org/wiki/Biometrichttp://en.wikipedia.org/wiki/Biometric

2.2. Score Normalization in Multimodal Score Normalization in Multimodal Biometric Systems (by Anil Jain , Karthik Biometric Systems (by Anil Jain , Karthik Nandakumar , Arun Ross)Nandakumar , Arun Ross)

3. Information fusion in biometrics (by Arun Ross , Anil Jain)

4. http://biometrics.cse.msu.edu/

http://en.wikipedia.org/wiki/Biometric

http://biometrics.cse.msu.edu/



ReferenceReference

5. http://www.cl.cam.ac.uk/~jgd1000/6. A. K. Jain and N. Duta, "Deformable matching of

hand shapes for verification", Proceedings of IEEE International Conference on Image Prcoessing, October 25-28, Kobe, Japan, 1999.

7. A. K. Jain and N. Duta, "Deformable matching of hand shapes for verification", Proceedings of IEEE International Conference on Image Prcoessing, October 25-28, Kobe, Japan, 1999.

8. A.K. Jain, Y. Chen and M. Demirkus, "Pores and Ridges: High Resolution Fingerprint Matching Using Level 3 Features", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 29, No. 1, pp. 15-27, January 2007.

http://www.cl.cam.ac.uk/~jgd1000/

fusion by biometrics 主講人：李佳明、陳明暘 指導教授：林維暘. outline...

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fusion by biometrics 主講人：李佳明、陳明暘指導教授：林維暘. outline...