Deep multi-biometric fuzzy commitment scheme: fusion methods and performance
Abstract Biometric cryptosystems enable privacy-preserving authentication using biometric data, such as fingerprints or iris scans. However, single modalities suffer from limited entropy, impacting both recognition performance and security. This work investigates the fusion of multiple biometric cha...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-07-01
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| Series: | EURASIP Journal on Information Security |
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| Online Access: | https://doi.org/10.1186/s13635-025-00206-6 |
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| author | Valentina Fohr Christian Rathgeb |
| author_facet | Valentina Fohr Christian Rathgeb |
| author_sort | Valentina Fohr |
| collection | DOAJ |
| description | Abstract Biometric cryptosystems enable privacy-preserving authentication using biometric data, such as fingerprints or iris scans. However, single modalities suffer from limited entropy, impacting both recognition performance and security. This work investigates the fusion of multiple biometric characteristics in a Deep Multi-biometric Fuzzy Commitment Scheme. In the experimental setup, we demonstrate how Deep Convolutional Neural Networks (DCNNs) are used to tackle the challenge of non-uniform representations by generating uniform embeddings. Uni-modal databases of iris and fingerprint embeddings, as well as the corresponding multi-biometric database, are employed for this purpose. Three fusion methods are proposed: concatenation, interleaving, and random shuffling within the fuzzy commitment scheme using error correction methods based on Hadamard and Reed-Solomon codes. The evaluation of performance and security reveals that random shuffling outperforms other methods like interleaving and concatenation in terms of recognition performance. Concatenation displayed the lowest performance. Finally, the findings are summarized and potential improvements are discussed. |
| format | Article |
| id | doaj-art-2785c370468b4a3fb4a9dade97a36ea6 |
| institution | DOAJ |
| issn | 2510-523X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Information Security |
| spelling | doaj-art-2785c370468b4a3fb4a9dade97a36ea62025-08-20T03:03:34ZengSpringerOpenEURASIP Journal on Information Security2510-523X2025-07-012025111510.1186/s13635-025-00206-6Deep multi-biometric fuzzy commitment scheme: fusion methods and performanceValentina Fohr0Christian Rathgeb1Hochschule DarmstadtHochschule DarmstadtAbstract Biometric cryptosystems enable privacy-preserving authentication using biometric data, such as fingerprints or iris scans. However, single modalities suffer from limited entropy, impacting both recognition performance and security. This work investigates the fusion of multiple biometric characteristics in a Deep Multi-biometric Fuzzy Commitment Scheme. In the experimental setup, we demonstrate how Deep Convolutional Neural Networks (DCNNs) are used to tackle the challenge of non-uniform representations by generating uniform embeddings. Uni-modal databases of iris and fingerprint embeddings, as well as the corresponding multi-biometric database, are employed for this purpose. Three fusion methods are proposed: concatenation, interleaving, and random shuffling within the fuzzy commitment scheme using error correction methods based on Hadamard and Reed-Solomon codes. The evaluation of performance and security reveals that random shuffling outperforms other methods like interleaving and concatenation in terms of recognition performance. Concatenation displayed the lowest performance. Finally, the findings are summarized and potential improvements are discussed.https://doi.org/10.1186/s13635-025-00206-6Biometric cryptosystemsFuzzy commitment schemeDeep neural networksInformation fusion |
| spellingShingle | Valentina Fohr Christian Rathgeb Deep multi-biometric fuzzy commitment scheme: fusion methods and performance EURASIP Journal on Information Security Biometric cryptosystems Fuzzy commitment scheme Deep neural networks Information fusion |
| title | Deep multi-biometric fuzzy commitment scheme: fusion methods and performance |
| title_full | Deep multi-biometric fuzzy commitment scheme: fusion methods and performance |
| title_fullStr | Deep multi-biometric fuzzy commitment scheme: fusion methods and performance |
| title_full_unstemmed | Deep multi-biometric fuzzy commitment scheme: fusion methods and performance |
| title_short | Deep multi-biometric fuzzy commitment scheme: fusion methods and performance |
| title_sort | deep multi biometric fuzzy commitment scheme fusion methods and performance |
| topic | Biometric cryptosystems Fuzzy commitment scheme Deep neural networks Information fusion |
| url | https://doi.org/10.1186/s13635-025-00206-6 |
| work_keys_str_mv | AT valentinafohr deepmultibiometricfuzzycommitmentschemefusionmethodsandperformance AT christianrathgeb deepmultibiometricfuzzycommitmentschemefusionmethodsandperformance |