Fingerprint Entropy and Identification Capacity Estimation Based on Pixel-Level Generative Modelling

Yankov, Metodi P.; Olsen, Martin Aastrup; Stegmann, Mikkel Bille; Christensen, Soren Sk.; Forchhammer, Søren

doi:10.1109/tifs.2019.2916406

Cited by 18 publications

(14 citation statements)

References 20 publications

(42 reference statements)

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“…However, the original templates of many traditional biometrics are not confidential. For instance, DNA [4], face [5], gait [31] and fingerprint [6] can be captured via lost hair, highresolution photography, depth camera and any touched surfaces, respectively. Once the original biometric template is stolen, it is compromised forever in all applications.…”

Section: B Comparison With Other Modalitiesmentioning

confidence: 99%

“…Furthermore, the biosignals of multiple individuals acquired by BSN devices are sent to a central server for further analysis in remote health monitoring, where personal identification is required. Compared with knowledge-based identification methods, such as a personal identification number (PIN) and password, biometrics-based ones such as DNA [4], face [5] and fingerprint [6], are relatively difficult to forge and reproduce if stolen. Previous studies have employed fingerprint recognition technique in implantable medical devices to ensure information security [7].…”

Section: Introductionmentioning

confidence: 99%

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Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification

Jiang

Liu

et al. 2021

IEEE J. Biomed. Health Inform.

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With the soaring development of body sensor network (BSN)-based health informatics, information security in such medical devices has attracted increasing attention in recent years. Employing the biosignals acquired directly by the BSN as biometrics for personal identification is an effective approach. Noncancelability and cross-application invariance are two natural flaws of most traditional biometric modalities. Once the biometric template is exposed, it is compromised forever. Even worse, because the same biometrics may be employed as tokens for different accounts in multiple applications, the exposed template can be used to compromise other accounts. In this work, we propose a cancelable and cross-application discrepant biometric approach based on high-density surface electromyogram (HD-sEMG) for personal identification. We enrolled two accounts for each user. HD-sEMG signals from the right dorsal hand under isometric contractions of different finger muscles were employed as biometrics tokens. Since isometric contraction, in contrast to dynamic contraction, requires no actual movement, the users' choice to login to different accounts is greatly protected against impostors. We realized a promising identification accuracy of 85.76% for 44 identities (22 subjects × 2 accounts) with training and testing data acquired 9 days apart. The high identification accuracy of different accounts for the same user demonstrates the promising cancelability and cross-application discrepancy of the proposed HD-sEMG-based biometrics. To the best of our knowledge, this is the first study to employ HD-sEMG in personal identification applications.

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Section: B Comparison With Other Modalitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification

Jiang

Liu

et al. 2021

IEEE J. Biomed. Health Inform.

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“…Recently, security technology is evolving along with artificial intelligence technology. From physical security to security using software, and by recognizing bio-information, it is moving toward simple and personalized security without risk of loss [ 1 , 2 , 3 ]. External environment security technology using bio-information gradually requires personal identification in a non-face to face method, and internal environment security technology carried by users is being carried out in portable smart devices and body-wearing wearable devices [ 4 , 5 , 6 ].…”

Section: Introductionmentioning

confidence: 99%

Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics

Choi

Pedrycz

et al. 2020

Sensors

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Although biometrics systems using an electrocardiogram (ECG) have been actively researched, there is a characteristic that the morphological features of the ECG signal are measured differently depending on the measurement environment. In general, post-exercise ECG is not matched with the morphological features of the pre-exercise ECG because of the temporary tachycardia. This can degrade the user recognition performance. Although normalization studies have been conducted to match the post- and pre-exercise ECG, limitations related to the distortion of the P wave, QRS complexes, and T wave, which are morphological features, often arise. In this paper, we propose a method for matching pre- and post-exercise ECG cycles based on time and frequency fusion normalization in consideration of morphological features and classifying users with high performance by an optimized system. One cycle of post-exercise ECG is expanded by linear interpolation and filtered with an optimized frequency through the fusion normalization method. The fusion normalization method aims to match one post-exercise ECG cycle to one pre-exercise ECG cycle. The experimental results show that the average similarity between the pre- and post-exercise states improves by 25.6% after normalization, for 30 ECG cycles. Additionally, the normalization algorithm improves the maximum user recognition performance from 96.4 to 98%.

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“…The final expression for the probability of a true-positive, we obtain by substitution of Equations (16) and (10) into (9).…”

Section: Quantification Of Condition Monitoring Uncertainty At Succesmentioning

confidence: 99%

“…Initially, only communication theory used the concept of Shannon entropy. However, subsequently, the Shannon entropy began to be used in many different fields of science and technology such as machine learning [2], biomedical informatics [3], reliability [4], prognostics [5], fault detection [6], condition monitoring [7], maintenance [8], fingerprint recognition [9], geosciences [10], fatigue damage modeling [11], and many others.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Condition Monitoring Decision Making by the Criterion of Minimum Entropy

Raza

Ulansky

2019

Entropy

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Condition-based maintenance (CBM) is a promising technique for a wide variety of deteriorating systems. Condition-based maintenance’s effectiveness largely depends on the quality of condition monitoring. The majority of CBM mathematical models consider perfect inspections, in which the system condition is assumed to be determined error-free. This article presents a mathematical model of CBM with imperfect condition monitoring conducted at discrete times. Mathematical expressions were derived for evaluating the probabilities of correct and incorrect decisions when monitoring the system condition at a scheduled time. Further, these probabilities were incorporated into the equation of the Shannon entropy. The problem of determining the optimal preventive maintenance threshold at each inspection time by the criterion of the minimum of Shannon entropy was formulated. For the first time, the article showed that Shannon’s entropy is a convex function of the preventive maintenance threshold for each moment of condition monitoring. It was also shown that the probabilities of correct and incorrect decisions depend on the time and parameters of the degradation model. Numerical calculations show that the proposed approach to determining the optimal preventive maintenance threshold can significantly reduce uncertainty when deciding on the condition of the monitoring object.

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Fingerprint Entropy and Identification Capacity Estimation Based on Pixel-Level Generative Modelling

Cited by 18 publications

References 20 publications

Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification

Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification

Recognition System Using Fusion Normalization Based on Morphological Features of Post-Exercise ECG for Intelligent Biometrics

Optimization of Condition Monitoring Decision Making by the Criterion of Minimum Entropy

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