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Biometric Authentication Systems 林維暘 中正大學 資訊 程學系 九十五學年度 第二學期 Biometric Authentication

Agenda § 2. 1 Introduction § 2. 2 Design Tradeoffs § 2. 3 Feature Extraction § 2. 4 Adaptive Classifiers Biometric Authentication 2

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. Biometric Authentication 3

Agenda § 2. 1 Introduction § 2. 2 Design Tradeoffs § 2. 3 Feature Extraction § 2. 4 Adaptive Classifiers Biometric Authentication 4

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 Biometric Authentication 5

2. 2 Design Tradeoffs • To evaluate a biometric system’s accuracy, the most adopted metrics are – False Rejection Rate (FRR) – False Acceptance Rate (FAR). Biometric Authentication 6

False Rejection Rate • FRR, or miss probability, is the percentage of FRR authorized individuals rejected by the system. • Sensitivity, a. k. a. True Positive Rate (TPR), is TPR the percentage that an authorized person is admitted. FRR = 1 - TPR Biometric Authentication 7

False Acceptance Rate • FAR, a. k. a. False Positive Rate (FPR), is the FAR FPR percentage that unauthorized individuals are accepted by the system. • Specificity, a. k. a. True Negative Rate (TNR), is TNR the percentage that an unauthorized person is correctly rejected. FAR = FPR = 1 - TNR Biometric Authentication 8

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 (ROC) curves ROC or by the Detection Error Tradeoff (DET) curves. DET • Tradeoff between FAR and FRR is adjusted by varying the threshold. Biometric Authentication 9

The ROC Curve Biometric Authentication 10

The ROC Curve Biometric Authentication 11

ROC and DET curves Biometric Authentication 12

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 day. – Physiological characteristics always remain the same. Biometric Authentication 13

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. Biometric Authentication 14

2. 2. 1 Accuracy vs. 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 Biometric Authentication 15

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 Biometric Authentication 16

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 Biometric Authentication 17

Central matcher & controller Central transact ion logging Central template database sensor Centralized system architecture user 18

Central transact ion logging Central template database Central controller matcher sensor Local DB matcher sensor user Local DB Distributed system architecture 19

2. 2. 3 Centralized vs. 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 Biometric Authentication 20

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] Biometric Authentication 21

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 image to a 20 -dimnesional feature vector. • Application types dictate the system architecture – e. g. , Central or local database Biometric Authentication 22

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 extraction – Symbol space: The symbols represent classes of objects. The mapping from feature space to symbol space is called classification Biometric Authentication 23

Compatibility between Feature Extractor and Classifier • Feature extraction – The most important stage in a recognition system – Represented by a mapping from instantiation space x to feature space v. x → v = f(x) Biometric Authentication 24

Compatibility between Feature Extractor and Classifier • Classification – The mapping from feature space to symbol space – A two-classifier • Discriminant function j(v) • j(v) > 0 if feature vector is extracted from an instantiation belonging to one class. • j(v) < 0 if feature vector is extracted from an instantiation belonging to the other class. Biometric Authentication 25

Compatibility between Feature Extractor and Classifier • In order to design an effective system, one needs to consider not only feature extraction but also classification. Raw Data Feature Extractor (e. g. speech waveform, fingerprint images, facial images) Pattern Classifier Feature Vectors (e. g. neural networks) Biometric Authentication Classification Decisions (e. g. ID of claimants, accept/reject) 26

Agenda § 2. 1 Introduction § 2. 2 Design Tradeoffs § 2. 3 Feature Extraction § 2. 4 Adaptive Classifiers Biometric Authentication 27

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 Biometric Authentication 28

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. Biometric Authentication 29

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) Biometric Authentication 30

2. 3. 2 Projection Methods for Dimension Reduction • Principal Component Analysis (PCA) PCA – 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 Biometric Authentication 31

2. 3. 2 Projection Methods for Dimension Reduction • Independent Component Analysis (ICA) ICA – ICA extracts components with higher-order statistical independence. – Kurtosis of a random variable is defined as Biometric Authentication 32

Independent Component Analysis 1. Gaussian: k(y) 3 2. Uniform: k(y) 1. 8 3. Binary: k(y) 1 Biometric Authentication 33

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 Biometric Authentication 34

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. Biometric Authentication 35

2. 3. 3 Feature Selection • Sometimes, only a few selected features would suffice. – In Hong Kong stock market, only 33 stocks are selected to calculate the Hang Seng index. • Note that unlike dimension reduction, there is no linear combination in the feature selection. Biometric Authentication 36

2. 3. 3 Feature Selection • Fisher Discriminant Analysis – Fisher discriminant J(xi) represents the ration of interclass distance to intra-class variance – m 1 i and m 2 i denote the means of xi belonging to class 1 and class 2, respectively. – s 1 i and s 2 i denote the variances of xi belonging to class 1 and class 2, respectively. Biometric Authentication 37

2. 3. 3 Feature Selection • Fisher Discriminant Analysis – The value of J(xi) provides a simple mean for feature selection. – The selected features will correspond to the indices with better discriminating capability. Biometric Authentication 38

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 Biometric Authentication 39

Project-Then-Cluster • We can adopt more sophisticated strategies such as cluster-the-project or project-then-cluster • Cluster-then-project – A projection aimed at separating two classes, each modeled by a GMM [404]. Biometric Authentication 40

PCA 2 -dimensional space (x-space) Model Selection User Interaction + EM + MDL • Cluster initialization • Clustering in x-space • Model validation EM (Probabilistic Clustering) • Clustering in t-space Gaussian Mixture Model Page 27 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 Biometric Authentication 41

Agenda § 2. 1 Introduction § 2. 2 Design Tradeoffs § 2. 3 Feature Extraction § 2. 4 Adaptive Classifiers Biometric Authentication 42

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 Biometric Authentication 43

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. Biometric Authentication 44

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. Biometric Authentication 45

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 Biometric Authentication 46

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 Biometric Authentication 47

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 Biometric Authentication 48

2. 4. 3 Criteria on Classifiers • The performance metrics of a learning algorithm – training accuracy: obtained from the training data accuracy – generalization accuracy: obtained from the testing accuracy data • There is usually a distinction between training and generalization accuracies. • High training accuracy does not necessarily yield good generalization accuracy. Biometric Authentication 49

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. Biometric Authentication 50

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. Biometric Authentication 51

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 Biometric Authentication 52

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. Biometric Authentication 53

Virtual pattern generation • Create additional training samples – 200 virtual images are generated from one facial image – Chimerical data Biometric Authentication 54

2. 5 Visual-Based Biometric Systems Biometric Authentication 55

2. 6 Audio-Based Biometric Systems Biometric Authentication 56