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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

Outline

• Introduction• Background• RHE Normalization• Experiment & Result• Conclusion

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Introduction

• Classical user authentication system– Identification card– Key– Etc..

• Biometric-based authentication system– Reliable verification– Reliable identification- Base on …

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Introduction

• Fingerprint

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• Face• Finger vein

Introduction

• Are these reliable ?

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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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Introduction

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Outline

• Introduction• Background• RHE Normalization• Experiment & Result• Conclusion

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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

• Z-Score normalization

• Also sensitive to outliners

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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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Outline

• Introduction• Background• RHE Normalization• Experiment & Result• Conclusion

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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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SVM-based Fusion

• Support Vector Machines(SVM)-based Fusion

• Using for classification

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Outline

• Introduction• Background• RHE Normalization• Experiment & Result• Conclusion

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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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#

Experiment & Result

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Experiment & Result

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Experiment & Result

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Experiment & Result

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Outline

• Introduction• Background• RHE Normalization• Experiment & Result• Conclusion

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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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Conclusion

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Rating_Total

Web application

Conclusion

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Thank you!

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Q&A