Protected Biometric Identification with Multiple Finger Vein

Choudhary, Swati K.; Naik, Ameya K.

doi:10.1109/asiancon55314.2022.9909050

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…Also, the locality sensitive hashing (LSH) [47] nature has recently been exploited and designed to obtain compact non-invertible features (e.g. [14], [32]). Similarly, protected templates are more likely to have the same hash collision compared to dissimilar ones.…”

Section: Biometric Template Protectionmentioning

confidence: 99%

Privacy-Preserving Multi-Biometric Indexing Based on Frequent Binary Patterns

Osorio-Roig,

González-Soler,

Rathgeb

et al. 2024

IEEE Trans.Inform.Forensic Secur.

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The development of large-scale identification systems that ensure the privacy protection of enrolled subjects represents a major challenge. Biometric deployments that provide interoperability and usability by including efficient multibiometric solutions are a recent requirement. In the context of privacy protection, several template protection schemes have been proposed in the past. However, these schemes seem inadequate for indexing (workload reduction) in biometric identification systems. More specifically, they have been used in identification systems that perform exhaustive searches, leading to a degradation of computational efficiency. To overcome these limitations, we present an efficient privacy-preserving multi-biometric identification system that retrieves protected deep cancelable templates and is agnostic with respect to biometric characteristics and biometric template protection schemes. To this end, a multi-biometric binning scheme is designed to exploit the low intra-class variation properties contained in the frequent binary patterns extracted from different types of biometric characteristics. Experimental results reported on publicly available databases using state-ofthe-art Deep Neural Network (DNN)-based embedding extractors show that the protected multi-biometric identification system can reduce the computational workload to approximately 57% (indexing up to three types of biometric characteristics) and 53% (indexing up to two types of biometric characteristics), while simultaneously improving the biometric performance of the baseline biometric system at the high-security thresholds 1 .

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Section: Biometric Template Protectionmentioning

confidence: 99%

Privacy-Preserving Multi-Biometric Indexing Based on Frequent Binary Patterns

Osorio-Roig,

González-Soler,

Rathgeb

et al. 2024

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

show abstract

“…Also, the locality sensitive hashing (LSH) [47] nature has recently been exploited and designed to obtain compact noninvertible features (e.g. [14], [32]) where similarly protected templates are more likely to have the same hash collision compared to dissimilar ones.…”

Section: Biometric Template Protectionmentioning

confidence: 99%

Indexing Protected Deep Face Templates by Frequent Binary Patterns

Osorio-Roig

Rathgeb

Shahreza

et al. 2022

2022 IEEE International Joint Conference on Biometrics (IJCB)

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The development of large-scale identification systems that ensure the privacy protection of enrolled subjects represents a major challenge. Biometric deployments that provide interoperability and usability by including efficient multibiometric solutions are a recent requirement. In the context of privacy protection, several template protection schemes have been proposed in the past. However, these schemes seem inadequate for indexing (workload reduction) in biometric identification systems. More specifically, they have been used in identification systems that perform exhaustive searches, leading to a degradation of computational efficiency. To overcome these limitations, we propose an efficient privacy-preserving multi-biometric identification system that retrieves protected deep cancelable templates and is agnostic with respect to biometric characteristics and biometric template protection schemes. To this end, a multi-biometric binning scheme is designed to exploit the low intra-class variation properties contained in the frequent binary patterns extracted from different types of biometric characteristics. Experimental results reported on publicly available databases using state-ofthe-art Deep Neural Network (DNN)-based embedding extractors show that the protected multi-biometric identification system can reduce the computational workload to approximately 57% (indexing up to three types of biometric characteristics) and 53% (indexing up to two types of biometric characteristics), while simultaneously improving the biometric performance of the baseline biometric system at the high-security thresholds. The source code of the proposed multi-biometric indexing approach together with the composed multi-biometric dataset, will be made available to the research community once the article is accepted.

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Efficient Cancelable Template Generation Based on Signcryption and Bio Hash Function

Rajasekar¹,

Saračević

Karabašević

et al. 2022

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Cancelable biometrics is a demanding area of research in which a cancelable template conforming to a biometric is produced without degrading the efficiency. There are numerous approaches described in the literature that can be used to generate these cancelable templates. These approaches do not, however, perform well in either the qualitative or quantitative perspective. To address this challenge, a unique cancelable template generation mechanism based on signcryption and bio hash function is proposed in this paper. Signcryption is a lightweight cryptographic approach that uses hyper elliptic curve cryptography for encryption and a bio hash function for generating signatures in this proposed method. The cancelable templates are generated from iris biometrics. The hybrid grey level distancing method is used for perfect iris feature extraction for the CASIA and IITD datasets. The proposed approach is compared against the existing state-of-the-art cancelable techniques. The resulting analysis reveals that the proposed method is efficient in terms of accuracy of 98.86%, with lower EER of 0.1%. The average minimum TPR and TNR of the proposed method is about 99.81%.

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Protected Biometric Identification with Multiple Finger Vein

Cited by 3 publications

References 23 publications

Privacy-Preserving Multi-Biometric Indexing Based on Frequent Binary Patterns

Privacy-Preserving Multi-Biometric Indexing Based on Frequent Binary Patterns

Indexing Protected Deep Face Templates by Frequent Binary Patterns

Efficient Cancelable Template Generation Based on Signcryption and Bio Hash Function

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