CCS '99 Proceedings of the 6th ACM conference on Computer and communications security
Enhancing security and privacy in biometrics-based authentication systems
IBM Systems Journal - End-to-end security
Overview of the Face Recognition Grand Challenge
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Face Recognition with Renewable and Privacy Preserving Binary Templates
AUTOID '05 Proceedings of the Fourth IEEE Workshop on Automatic Identification Advanced Technologies
Fuzzy Extractors: How to Generate Strong Keys from Biometrics and Other Noisy Data
SIAM Journal on Computing
Threshold-optimized decision-level fusion and its application to biometrics
Pattern Recognition
Practical biometric authentication with template protection
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Fingerprint-Based Fuzzy Vault: Implementation and Performance
IEEE Transactions on Information Forensics and Security
A 3D face recognition algorithm using histogram-based features
EG 3DOR'08 Proceedings of the 1st Eurographics conference on 3D Object Retrieval
"3D Face": biometric template protection for 3d face recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Robust extraction of secret bits from minutiae
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Fast and Accurate 3D Face Recognition
International Journal of Computer Vision
Investigating fusion approaches in multi-biometric cancellable recognition
Expert Systems with Applications: An International Journal
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The popularity of biometrics and its widespread use introduces privacy risks. To mitigate these risks, solutions such as the helper-data system, fuzzy vault, fuzzy extractors, and cancelable biometrics were introduced, also known as the field of template protection. In parallel to these developments, fusion of multiple sources of biometric information have shown to improve the verification performance of the biometric system. In this work we analyze fusion of the protected template from two 3D recognition algorithms (multi-algorithm fusion) at feature-, score-, and decision-level. We show that fusion can be applied at the known fusion-levels with the template protection technique known as the Helper-Data System. We also illustrate the required changes of the Helper-Data System and its corresponding limitations. Furthermore, our experimental results, based on 3D face range images of the FRGC v2 dataset, show that indeed fusion improves the verification performance.