Towards cancelable multi-biometrics based on bloom filters: a case study on feature level fusion of face and iris

Rathgeb, Christian; Gomez‐Barrero, Marta; Busch, Christoph; Galbally, Javier; Fiérrez, Julián

doi:10.1109/iwbf.2015.7110225

Cited by 49 publications

(33 citation statements)

References 23 publications

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“…Rathgeb et al [20] work proposes a Bloom filter-based transforms to protect templates in face and iris samples. It builds two matrices arranged in two-dimensional binary code, divided into blocks of equal size consisting of w F (w I ) bits.…”

Section: B Biometric Security and Privacy Workmentioning

confidence: 99%

“…Lai et al [16] YES NO YES NO Lai et al [17] YES NO YES NO Nagar et al [15] YES NO YES NO Nassir and Perumal [14] YES YES NO NO Rathgeb et al [20] YES NO YES NO Kumar and Kumar [12] YES NO YES NO Li et al [13] YES NO YES NO Kaur and Sofat [35] YES NO NO NO Zhou and Ren [11] YES NO YES NO Toli and Preneel [10] YES YES NO NO Kaur and Khanna [18] YES NO YES NO Our proposed scheme YES YES YES YES complete the purpose task. Also, it allows us to use the IDN algorithm created, ensuring the anonymity of the records.…”

Section: Interoperabilitymentioning

confidence: 99%

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Improving Data Security, Privacy, and Interoperability for the IEEE Biometric Open Protocol Standard

Filho

Sousa

et al. 2022

IEEE Access

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Section: B Biometric Security and Privacy Workmentioning

confidence: 99%

Section: Interoperabilitymentioning

confidence: 99%

Improving Data Security, Privacy, and Interoperability for the IEEE Biometric Open Protocol Standard

Filho

Sousa

et al. 2022

IEEE Access

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“…The performance of proposed approach goes lower to higher w.r.t high dimension bitmaps as the number of the available bits increased. Experiment-2 : Inspired from the earlier works [17], [21], we propose to concatenate the deep feature vectors of major knuckle, minor knuckle and nail to quantitatively evaluate the performance of cancelable finger dorsal biometrics. This test has been performed on PolyU FKI database with the same testing strategy.…”

Section: Algorithm 1: Verification Algorithmmentioning

confidence: 99%

FDFNet: A Secure Cancelable Deep Finger Dorsal Template Generation Network Secured via. Bio-Hashing

Singh

Arora

Patel

et al. 2019

2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA)

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Present world has already been consistently exploring the fine edges of online and digital world by imposing multiple challenging problems/scenarios. Similar to physical world, personal identity management is very crucial inorder to provide any secure online system. Last decade has seen a lot of work in this area using biometrics such as face, fingerprint, iris etc. Still there exist several vulnerabilities and one should have to address the problem of compromised biometrics much more seriously, since they cannot be modified easily once compromised. In this work, we have proposed a secure cancelable finger dorsal template generation network (learning domain specific features) secured via. Bio-Hashing. Proposed system effectively protects the original finger dorsal images by withdrawing compromised template and reassigning the new one. A novel Finger-Dorsal Feature Extraction Net (FDFNet) has been proposed for extracting the discriminative features. This network is exclusively trained on trait specific features without using any kind of pre-trained architecture. Later Bio-Hashing, a technique based on assigning a tokenized random number to each user, has been used to hash the features extracted from FDFNet. To test the performance of the proposed architecture, we have tested it over two benchmark public finger knuckle datasets: PolyU FKP and PolyU Contactless FKI. The experimental results shows the effectiveness of the proposed system in terms of security and accuracy.

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“…Rathgeb et al [35] proposed an alignment free iris keybinding scheme with concealable transforms. They adopted Indexing-First-One (IFO) hashing to achieve non-invertible and cancelable transformation for biometrics and the cryptographic key-binding.…”

Section: A Vulnerabilities Attacks Of Biometric Template In the Datamentioning

confidence: 99%

Developing an Algorithm for Securing the Biometric Data Template in the Database

Habibu¹,

Luhanga²,

Sam³

2019

IJACSA

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In the current technology advancement, biometric template provides a dependable solution to the problem of user verification in an identity control system. The template is saved in the database during the enrollment and compared with query information in the verification stage. Serious security and privacy concerns can arise, if raw, unprotected data template is saved in the database. An attacker can hack the template information in the database to gain illicit access. A novel approach of encryption-decryption algorithm utilizing a design pattern of Model View Template (MVT) is developed to secure the biometric data template. The model manages information logically, the view shows the visualization of the data, and the template addresses the data migration into pattern object. The established algorithm is based on the cryptographic module of the Fernet key instance. The Fernet keys are combined to generate a multiFernet key to produce two encrypted files (byte and text file). These files are incorporated with Twilio message and securely preserved in the database. In the event where an attacker tries to access the biometric data template in the database, the system alerts the user and stops the attacker from unauthorized access, and cross-verify the impersonator based on the validation of the ownership. Thus, helps inform the users and the authority of, how secure the individual biometric data template is, and provided a high level of the security pertaining the individual data privacy.

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Towards cancelable multi-biometrics based on bloom filters: a case study on feature level fusion of face and iris

Cited by 49 publications

References 23 publications

Improving Data Security, Privacy, and Interoperability for the IEEE Biometric Open Protocol Standard

Improving Data Security, Privacy, and Interoperability for the IEEE Biometric Open Protocol Standard

FDFNet: A Secure Cancelable Deep Finger Dorsal Template Generation Network Secured via. Bio-Hashing

Developing an Algorithm for Securing the Biometric Data Template in the Database

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