Biometric Authentication Systems
林維暘中正大學 資訊工程學系九十五學年度 第二學期
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Agenda
§ 2.1 Introduction2.1 Introduction
§ 2.2 Design Tradeoffs
§ 2.3 Feature Extraction
§ 2.4 Adaptive Classifiers
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Introduction
• There is a rapidly increasing interest in the development of commercial systems for biometric authentication applications.
• The objective of a commercial system is to satisfy security requirement while incurring minimal cost.
• This chapter discusses system deployment requirements as well as critical design tradeoffs.
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Agenda
§ 2.1 Introduction
§ 2.2 Design Tradeoffs2.2 Design Tradeoffs
§ 2.3 Feature Extraction
§ 2.4 Adaptive Classifiers
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2.2 Design Tradeoffs
§ 2.2.1 Accuracy vs. Intrusiveness
§ 2.2.2 Recognition vs. Verification
§ 2.2.3 Centralized vs. Distributed
§ 2.2.4 Processing Speed
§ 2.2.5 Storage Requirements
§ 2.2.6 Compatibility between Feature Extractor and Classifier
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2.2 Design Tradeoffs
• To evaluate a biometric system’s accuracy, the most adopted metrics are– False Rejection Rate (FRR)– False Acceptance Rate (FAR).
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False Rejection Rate
• FRRFRR, or miss probability, is the percentage of authorized individuals rejected by the system.
• Sensitivity, a.k.a. True Positive Rate (TPRTPR), is the percentage that an authorized person is admitted.
FRR = 1 - TPR
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False Acceptance Rate
• FARFAR, a.k.a. False Positive Rate (FPRFPR), is the percentage that unauthorized individuals are accepted by the system.
• Specificity, a.k.a. True Negative Rate (TNRTNR), is the percentage that an unauthorized person is correctly rejected.
FAR = FPR = 1 - TNR
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The ROC Curve
• A good authentication system should have both low FRR and low FAR.
• Typically, the tradeoff is illustrated by so-called Receiver Operation Characteristic (ROCROC) curves or by the Detection Error Tradeoff (DETDET) curves.
• Tradeoff between FAR and FRR is adjusted by varying the threshold.
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The ROC Curve
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The ROC Curve
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ROC and DET curves
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2.2.1 Accuracy vs. Intrusiveness
• Physiological characteristics (e.g., fingerprint and iris) generally provide higher accuracy than behavioral features (e.g., voice and signature).– Behavioral features can change from daty to d
ay.– Physiological characteristics always remain th
e same.
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2.2.1 Accuracy vs. Intrusiveness
• If a security system makes users feel uncomfortable, then it is intrusive.
• For low security level environments (e.g. apartments, hotels), an intrusive system is highly undesirable.
• On the other hand, intrusive systems are commonly deployed in high security areas.
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2.2.1 Accuracy vs. Intrusiveness
Intrusiveness Convenience Error rate
Face No Good 10-1 ~ 10-3
Palm No? Middle < 10-3
Fingerprint Yes Middle 10-2 ~ 10-6
Iris Yes Bad < 10-6
Voice No Middle 10-1 ~ 10-2
Signature No Bad 10-1 ~ 10-3
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2.2.2 Identification vs. Verification
• Identification– Search a database for an acceptable match– Higher computational cost– Higher error rate
• Verification– Verify the identity of a user– Greatly reduced FAR– Slightly increased FRR
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2.2.3 Centralized vs. Distributed
• Three major components in a biometric system– Sensor– Pattern matcher– Controller
• These pieces can be configured in various ways.– Centralized– Distributed
Centralized system architecture 18
Central matcher
& controller Central
template database
Central transaction logging
sensor
sensor sensor
sensor
user
Distributed system architecture 19
Central controller
Central template database
Central transaction logging
sensorsensor
sensorsensor
Local DB Local DB
Local DB Local DB
matcher matcher
matcher matcher
user
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2.2.3 Centralized vs. Distributed
Distributed System Centralized System
Less communication loading More communication loading
Lower risk of system failure Higher risk of system failure
Maintenance is more complex
Less management issues
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2.2.4 Processing Speed
• If a gateway control system takes on hour to process one entry request, it is useless no mater how accurate it is.
• Fingerprint identification system– 18 types of fingerprint features– Error rate of 10-10 can be achieved– Accuracy is usually sacrificed for speed
[198,295]
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2.2.5 Data Storage Requirements
• In most scenarios, the size of raw data is too large to store.
• Raw data is compressed into feature vectors with much smaller dimension.– Pentland et al. [272] compress a 256 x 256 im
age to a 20-dimnesional feature vector.
• Application types dictate the system architecture– e.g., Central or local database
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2.2.6 Compatibility between Feature Extractor and Classifier
• A recognition system involves mapping between the following spaces. – Instantiation space: A symbol is instantiated into an
object. A symbol may have different instantiations.– Feature space: The mapping from instantiation space
to feature space is called feature extractionfeature extraction.– Symbol space: The symbols represent classes of
objects. The mapping from feature space to symbol space is called classificationclassification.
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Compatibility between Feature Extractor and Classifier
• Feature extraction– The most important stage in a recognition syst
em– Represented by a mapping from instantiation
space x to feature space v.
x → v = f(x)
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Compatibility between Feature Extractor and Classifier
• Classification– The mapping from feature space to symbol sp
ace– A two-class classifier
• Discriminant function (v)• (v) > 0 if feature vector is extracted from an insta
ntiation belonging to one class.• (v) < 0 if feature vector is extracted from an insta
ntiation belonging to the other class.
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Compatibility between Feature Extractor and Classifier
• In order to design an effective system, one needs to consider not only feature extraction but also classification.
Feature Extractor
Pattern Classifier
(e.g. neural networks)Raw Data
(e.g. speech waveform, fingerprint images, facial images)
Feature Vectors
Classification Decisions
(e.g. ID of claimants, accept/reject)
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Agenda
§ 2.1 Introduction
§ 2.2 Design Tradeoffs
§ 2.3 Feature Extraction2.3 Feature Extraction
§ 2.4 Adaptive Classifiers
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2.3 Feature Extraction
§ 2.3.1 Criteria of feature extraction
§ 2.3.2 Projection methods for dimension reduction
§ 2.3.3 Feature selection
§ 2.3.4 Clustering methods
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2.3.1 Criteria of Feature Extraction
• Data compression– Only vital representations are extracted.
• Informative ness– The characteristics essential for the intended
applications should be best described.
• Invariance– The dependency on environmental conditions should
be minimized.
• Ease of processing– A cost-effective implementation should be feasible.
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2.3.1 Criteria of Feature Extraction
• Two approaches are often adopted to obtain compressed representation.– Dimension reduction by projection onto linear
subspace– Data clustering (Chapter 3)
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2.3.2 Projection Methods for Dimension Reduction
• Principal Component Analysis (PCAPCA)– A mapping from Rn to Rm, n > m– Mathematically, the PCA is to find a matrix W
such that
y = W x, where W is an mxn matrix
– The W is formed by the m eigenvectors corresponding to the largest m eigenvalues
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2.3.2 Projection Methods for Dimension Reduction
• Independent Component Analysis (ICAICA)– ICA extracts components with higher-order
statistical independence.– Kurtosis of a random variable is defined as
22
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YE
YEYk
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Independent Component Analysis
1. Gaussian:
2. Uniform:
3. Binary:
k(y) 3
k(y) 1.8
k(y) 1
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Independent Component Analysis
• PCA maximizes the second-order covariance.
• ICP maximizes the fourth-order kurtosis.– An advantage of using ICA is that kurtosis
function is scale invariant.– The most discriminative independent
component
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wx
wxw E
E
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Independent Component Analysis
• Mathematically, the ICA is to find a matrix W such that
y = W x, where W is an mxn matrix
– y contains the m most discriminative independent components.
– The W is formed by the m independent row vectors wi, which can be extracted sequentially.
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2.3.3 Feature Selection
• Sometimes, only a few selected features would suffice.– In Hong Kong stock market, only 33 stocks ar
e selected to calculate the Hang Seng index.
• Note that unlike dimension reduction, there is no linear combination in the feature selection.
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2.3.3 Feature Selection
• Fisher Discriminant Analysis– Fisher discriminant J(xi) represents the ration of inter-
class distance to intra-class variance
– 1i and 2i denote the means of xi belonging to class 1 and class 2, respectively.
– 1i and 2i denote the variances of xi belonging to class 1 and class 2, respectively.
22
21
221 )(
)(ii
iiixJ
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2.3.3 Feature Selection
• Fisher Discriminant Analysis– The value of J(xi) provides a simple mean for f
eature selection.– The selected features will correspond to the in
dices with better discriminating capability.
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2.3.4 Clustering Methods: GMM
• Most biometric data cannot be adequately modeled by a single–cluster Gaussian model.
• Gaussian Mixture Model (GMM) provides a more flexible model for describing the distribution of biometric data.– K-means or EM algorithms– Optimal number of clusters
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Project-Then-Cluster
• We can adopt more sophisticated strategies such as cluster-the-projectcluster-the-project or project-then-clusterproject-then-cluster.
• Cluster-then-project– A projection aimed at separating two classes,
each modeled by a GMM [404].
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PCA
Model Selection
User Interaction + EM + MDL
2-dimensional space (x-space)
EM
(Probabilistic Clustering)
•Cluster initialization
•Clustering in x-space
•Model validation
Gaussian Mixture Model•Clustering in t-space
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Fig 2.7 An illustration of the project-then-cluster approach. Projection of data from t-space to x-space, then after clustering in the lower-dimension subspace, trace the membership information back to the t-space
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Agenda
§ 2.1 Introduction
§ 2.2 Design Tradeoffs
§ 2.3 Feature Extraction
§ 2.4 Adaptive Classifiers2.4 Adaptive Classifiers
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2.4 Adaptive Classifiers
§ 2.4.1 Neural networks
§ 2.4.2 Training strategies
§ 2.4.3 Criteria on classifiers
§ 2.4.4 Availability of training samples
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2.4 Adaptive Classifiers
• Statistical approach– Each class is modeled by a normal
distribution– Using prior probabilities, one can compute the
posterior probabilities of each person, conditioned on an observation.
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2.4.1 Neural Networks
• A neural work is a simulation of the nervous system that contains neuron unit communicating with one another via axon connections.
• By combining a vast number of simple neurons, it is possible to achieve a sophisticated task.
• Neural networks for biometric applications are discussed in Chapter 5, 6, and 7.
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2.4.2 Training Strategies
• Neural networks can learn rules from a collection of examples.
• The ability to learn from examples is a major advantage of neural networks.
• Two types of learning:– Supervised– Unsupervised
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2.4.2 Training Strategies
• Supervised learning– A neural network is provided with a training
set with labels (the “teacher values”).– The parameters are determined so that the
system can produce answers as close as possible to the teacher values
– e.g., OCR and speaker recognition
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2.4.2 Training Strategies
• Unsupervised learning– Explore the underlying rules from an
unlabeled training set– Used in the applications where teacher values
are expensive or difficult to obtain
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2.4.3 Criteria on Classifiers
• The performance metrics of a learning algorithm– training accuracytraining accuracy: obtained from the training data– generalization accuracygeneralization accuracy: obtained from the testing
data
• There is usually a distinction between training and generalization accuracies.
• High training accuracy does not necessarily yield good generalization accuracy.
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2.4.3 Criteria on Classifiers
• Invariance and noise resilience– Minimize the dependency on environmental
conditions.– Tolerate noise corruption because noise is
inevitable in practical applications.
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2.4.3 Criteria on Classifiers
• Cost-effective system implementation– A cost-effective platform should be
considered.– Emphasis should also be placed on the
issues of system integration.
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2.4.4 Availability of Training Samples
• The availability of training data is of critical concern.
• Solutions to the training sample deficiency problem– Conduct an intensive study on the nature of
the selected biometric.– Virtual pattern generation
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Intensive study
• An example: fingerprint– The relative positions between various
minutiae are the discriminative features.– The resulting feature vectors could be
separated by simple classifiers.– There is no need to use example to tell the
system which features should be extracted.
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Virtual pattern generation
• Create additional training samples– 200 virtual images are generated from one
facial image– Chimerical data
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2.5 Visual-Based Biometric Systems
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2.6 Audio-Based Biometric Systems