Alignment-free cancelable fingerprint templates with dual protection

Shahzad, Muhammad; Wang, Song; Deng, Guang; Yang, Wencheng

doi:10.1016/j.patcog.2020.107735

Cited by 38 publications

(24 citation statements)

References 33 publications

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“…The description of these metrics is as follows. False acceptance rate (FAR): The FAR is the probability of mistaking biometric samples from different subjects to be from the same subject [ 56 ]. False rejection rate (FRR): The FRR is the probability of mistaking biometric samples from the same subject to be from different subjects [ 56 ].…”

Section: Biometrics Overviewmentioning

confidence: 99%

“… False acceptance rate (FAR): The FAR is the probability of mistaking biometric samples from different subjects to be from the same subject [ 56 ]. False rejection rate (FRR): The FRR is the probability of mistaking biometric samples from the same subject to be from different subjects [ 56 ]. Equal error rate (EER): The EER is the error rate when FAR and FRR have the same value [ 56 ].…”

Section: Biometrics Overviewmentioning

confidence: 99%

“… False rejection rate (FRR): The FRR is the probability of mistaking biometric samples from the same subject to be from different subjects [ 56 ]. Equal error rate (EER): The EER is the error rate when FAR and FRR have the same value [ 56 ]. The FAR and FRR are inversely related, which means that when one increases, the other should decrease.…”

Section: Biometrics Overviewmentioning

confidence: 99%

“…False acceptance rate (FAR): The FAR is the probability of mistaking biometric samples from different subjects to be from the same subject [ 56 ].…”

Section: Biometrics Overviewmentioning

confidence: 99%

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Biometrics for Internet-of-Things Security: A Review

Yang

Wang

Sahri

et al. 2021

Sensors

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The large number of Internet-of-Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric-based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric-cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state-of-the-art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward-looking issues and future research directions.

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Section: Biometrics Overviewmentioning

confidence: 99%

Section: Biometrics Overviewmentioning

confidence: 99%

Section: Biometrics Overviewmentioning

confidence: 99%

“…False acceptance rate (FAR): The FAR is the probability of mistaking biometric samples from different subjects to be from the same subject [ 56 ].…”

Section: Biometrics Overviewmentioning

confidence: 99%

See 2 more Smart Citations

Biometrics for Internet-of-Things Security: A Review

Yang

Wang

Sahri

et al. 2021

Sensors

Self Cite

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“…Encryption keys provide increased protection to the biometric cryptosystems. In these cryptosystems, the genuine biometric features are not kept directly in the cloud, but they are initially processed and converted into deformed templates called noise templates (encrypted images) [18,19]. Biometric template techniques are divided into helper-databased schemes and cancelable biometric schemes.…”

Section: Introductionmentioning

confidence: 99%

Efficient and Secure Cancelable Biometric Authentication Framework Based on Genetic Encryption Algorithm

et al. 2021

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Various cancelable biometric techniques have been proposed to maintain user data security. In this work, a cancelable biometric framework is introduced to satisfy user data security and keeping the original biometric template safe away from intruders. Thus, our main contribution is presenting a novel authentication framework based on the evolutionary Genetic Algorithm (GA)-based encryption technique. The suggested framework produces an entirely unrecognized biometric template by hiding the whole discriminative features of biometric templates; this is with exploiting the outstanding characteristics of the employed Genetic operations of the utilized encryption technique. Firstly, the GA initiates its search from a population of templates, not a single template. Secondly, some statistical operators are used to exploit the resulting initial population to generate successive populations. Finally, the crossover and mutation operations are performed to produce the ultimate cancelable biometric templates. Different biometric databases of the face and fingerprint templates are tested and analyzed. The proposed cancelable biometric framework achieves appreciated sensitivity and specificity results compared to the conventional OSH (Optical Scanning Holography) algorithm. It accomplishes recommended outcomes in terms of the AROC (Area under the Receiver Operating Characteristic) and the probability correlation distribution between the original biometrics and the encrypted biometrics stored in the database. The experimental results prove that the proposed framework achieves excellent results even if the biometric system suffers from different noise ratios. The proposed framework achieves an average AROC value of 0.9998, an EER (Equal Error Rate) of 2.0243×10 -4 , FAR (False Acceptance Rate) of 4.8843×10 -4 , and FRR (False Rejection Rate) of 2.2693×10 -4 .

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