1 performance evaluation of score level fusion in multimodal biometric systems web computing...
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Performance Evaluation of Score Level Fusion in Multimodal Biometric Systems
Web Computing LaboratoryComputer Science and Information Engineering
DepartmentFu Jen Catholic University
Speaker: Wei Tin LaiSpeaker: Wei Tin LaiAdvisor Prof. Hsing Mei Advisor Prof. Hsing Mei
Introduction
• Classical user authentication system– Identification card– Key– Etc..
• Biometric-based authentication system– Reliable verification– Reliable identification- Base on …
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Introduction
• Unibiometric system– Noisy data– Lack of distinctiveness of the trait
• Multimodal biometric system– Combine multiple biometric samples (face, fingerprint..)– Normalization– Fusion
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Background
• Normalization– Min-Max normalization
– max(X):maximum value of the raw matching scores
– min(X):minimum value of the raw matching scores
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Background
• Min-Max normalization– Drawback: Sensitive to outliers
Original data
After normalization
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Background
• Tanh-Estimators normalization
• Oop…so many parameters...
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‧Too many parameters have to be determined
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RHE Normalization
• Reduction of High-scores Effect (RHE) Normalization
• Two observations– Normalization will causes loss of information
– Suffer mainly from the ‘LOW’ genuine scores instead of ‘HIGH’ imposter scores
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RHE Normalization
• With these observation, RHE normalization procedure as follows:
1)Use the raw data if the range of data is similar.(Will not normalize the data).
2)Modify the min-max normalization formula to fit the ‘LOW’ genuine scores .
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RHE Normalization
• RHE normalization formula
– X: the distribution of all raw scores.– X*:the distribution of all genuine raw scores
• Advantage: Performance will be increased.• Drawback: ‘low’ impostor scores will also
uplifted.
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Sum Rule-based Fusion
• The fused score(fs) is evaluated using following formula:
W: weight
• In here we set all the weight to be 1
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Sum Rule-based Fusion
• The fused score(fs) is evaluated using following formula:
W: weight
• In here we set all the weight to be 1
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Experiment & Result
• National Institute of Standard and Technology(NIST) Biometric Score Set(BSSR1)
• Databases– NIST-Multimodal
• Face score(Matcher C ,Matcher G)• Fingerprint(Left index finger ,Right index finger)
– NIST-Face• Face score(Matcher C, Matcher G)
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Experiment & Result
• Databases(cont.)– NIST-Fingerprint
• Left index finger• Right index finger
– Merged database of fingerprint, face and finger vein
• Face scores(Matcher G)• Fingerprint scores(Right index finger)• Finger vein
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Experiment & Result
• Genuine Accept Rate(GAR )
• False Accept Rate(FAR)
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scoresgenuine
scoresgenuineacceptedcorrectly
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scoresimpostor
scoresimpostoracceptedfalsely
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Conclusion
• Multimodal biometric modal has better performance than Unimodal biometric modal
• SVM fusion is better than Sum rule-based fusion if the parameters are determined
• RHE has better performance!
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