introduction to biometricshomes.di.unimi.it/donida/biometricsphd/lecture1_donida-labati.pdf ·...
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Image Processing forBiometric ApplicationsRuggero Donida Labati
Introduction to Biometrics
Academic year 2016/2017
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• Basic concepts on biometrics• Biometric recognition• Evaluation of biometric systems
Summary
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Basic concepts on biometrics
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Biometrics is defined by the International Organization for Standardization (ISO) as“the automated recognition of individuals based on their behavioral and biological characteristics”
Biometrics
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Biometric traits
• Traditional recognition methods:key, password, smartcard, token
• Biometrics:- behavioral
voice gait signature keystroke
- physiological fingerprint iris hang geometry palmprint palmevein ear ECG DNA
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• Human characteristic1. Universality2. Distinctiveness3. Permanence4. Collectability
• Technology1. Performance2. Acceptability3. Circumvention
Characteristics of biometric traits
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Qualitative evaluation of biometric traits
Trait Univ. Uniq. Perm. Coll. Perf. Acc. Circ.Face H L M H L H LFingerprint M H H M H M HHand geometry M M M H M M MKeystrokes L L L M L M MHand vein M M M M M M HIris H H H M H L HRetinal scan H H M L H L HSignature L L L H L H LVoice M L L M L H LFacethermograms H H L H M H HOdor H H H L L M LDNA H H H L H L LGate M L L H L H MEar M M H M M H M
A. Jain, A. Ross, and S. Prabhakar, “An introduction to biometric recognition,” IEEE Trans. on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4 –20, Jan. 2004.
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Biometric traits in real applications
International Biometric Group, New York, NY; 1.212
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Applications (1/3)
2004 Summer OlympicsDisney world
Border control
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Applications (2/3)
ATM
Shops
Surveillance
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LiveGripTM Advanced Biometrics, Inc
Hitachi - grip-type finger vein authenticationhttp://www.hitachi.com
Smartphones
Applications (3/3)
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• Soft biometric traits are characteristics that provide some information about the individual, but lack the distinctiveness and permanence to sufficiently differentiate any two individuals
• Continuous or discrete
Soft biometrics
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Biometric recognition
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Patter recognition systems
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Patter recognition and biometrics
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• Verificationo The verification involves confirming or
denying a person’s claimed identity
• Identificationo In the identification mode, the biometric
system has to establish a person’s identity by comparing the acquired biometric data with the information related to a set of individuals, performing a one-to-manycomparison
o Positiveo Negative
Verification / Identification
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Enrollment
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Verification
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Identification
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Decision
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Modules of biometric systems
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Preprocessing
Segmentation Enhancement
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Evaluation of biometric systems
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Evaluation strategies
Technology
Scenario
OperationalNIST
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Aspects to be evaluated
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Accuracy evaluation:genuine and impostor comparisons (1/2)
Genuine comparison
Impostor comparison
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Genuine scoresS(X1_1, X1_2) = 0.7 S(X1_1, X1_3) = 0.8 S(X2_1, X2_2) = 0.4 S(X2_1, X2_3) = 0.5
Impostor scoresS(X1_1, X3_2) = 0.11 S(X4_1, X3_1) = 0.21S(X5_2, X1_2) = 0.001S(X2_2, X1_2) = 0.19
Accuracy evaluation:genuine and impostor comparisons (2/2)
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• False match rate (FMR): the probability that the system incorrectly matches the input pattern to a non-matching template in the database. It measures the percent of invalid inputs that are incorrectly accepted. Type 1 error
• False non-match rate (FNMR): the probability that the system fails to detect a match between the input pattern and a matching template in the database. It measures the percent of valid inputs that are incorrectly rejected.Type 2 error
Accuracy evaluation:FMR and FNMR
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Accuracy evaluation:DET and ROC
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• Failure to acquire (FTA)• Failure to enroll (FTE)• False acceptance rate (FAR): similar to FMR,
but used for the complete biometric system (not only the algorithms)
• False rejection rate (FRR): similar to FNMR, but used for the complete biometric system (not only the algorithms)
• Equal error rate (EER): the ideal point in which FMR = FNMR
Accuracy evaluation:other figures of merit
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Accuracy evaluation:identification