Fusion of electrocardiogram with unobtrusive biometrics: An efficient individual authentication system

Singh, Yatindra Nath; Singh, Sanjay Kumar; Gupta, Phalguni

doi:10.1016/j.patrec.2012.03.010

Cited by 69 publications

(28 citation statements)

References 9 publications

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“…However, the ECG signal can be combined with other biometric (e.g., face and fingerprint) and provide an excellent source of supplementary information in a multibiometric framework. Singh et al [27] have proposed a multibiometric system that fuses the ECG signal with face and fingerprint biometrics, effectively. Transformation-based score fusion technique is used to evaluate the effectiveness of the aforementioned system.…”

Section: Resultsmentioning

confidence: 99%

“…Although, the methods using the ECG as a biometric may not offer adequate accuracy [26,27], the ECG information can assist in improving the security of the system with other biometrics in a multibiometric framework. Singh et al [27] have proposed a multibiometric system which is obtained from the fusion of the ECG signal with, the face and the fingerprint biometrics for robust persons authentication.…”

Section: Supporting Factorsmentioning

confidence: 99%

“…Singh et al [27] have proposed a multibiometric system which is obtained from the fusion of the ECG signal with, the face and the fingerprint biometrics for robust persons authentication. The performance of the proposed system has reported optimum, and that the equal error rate (EER) of the fused system is found to be 0.2% as compared to an individual system of the ECG that has an ERR of 10.8% while the EER of the face and the fingerprint systems are 4.5% and 2.1%, respectively.…”

Section: Supporting Factorsmentioning

confidence: 99%

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Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Singh

Ray

2012

ISRN Signal Processing

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This paper presents the effectiveness of bioelectrical signals such as the electrocardiogram (ECG) and the electroencephalogram (EEG) for biometric applications. Studies show that the impulses of cardiac rhythm and electrical activity of the brain recorded in ECG and EEG, respectively; have unique features among individuals, therefore they can be suggested to be used as biometrics for identity verification. The favourable characteristics to use the ECG or EEG signals as biometric include universality, measurability, uniqueness and robustness. In addition, they have the inherent feature of vitality that signifies the life signs offering a strong protection against spoof attacks. Unlike conventional biometrics, the ECG or EEG is highly confidential and secure to an individual which is difficult to be forged. We present a review of methods used for the ECG and EEG as biometrics for individual authentication and compare their performance on the datasets and test conditions they have used. We illustrate the challenges involved in using the ECG or EEG as biometric primarily due to the presence of drastic acquisition variations and the lack of standardization of signal features. In order to determine the large-scale performance, individuality of the ECG or EEG is another challenge that remains to be addressed.

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Section: Resultsmentioning

confidence: 99%

Section: Supporting Factorsmentioning

confidence: 99%

Section: Supporting Factorsmentioning

confidence: 99%

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Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Singh

Ray

2012

ISRN Signal Processing

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“…This unattended situation will provoke imposters for spoof attacks with fake biometric samples for obtaining undue access to the genuine users account. Existing biometric modalities, such as fingerprint, iris, and face, are vulnerable [18] in such unattended scenarios, as an imposter will have sufficient opportunity to spoof a target client. For example, it has been shown that it is possible to spoof varieties of fingerprint technology by gummy fingers [19].…”

Section: Introductionmentioning

confidence: 99%

Heartbeat Biometrics for Remote Authentication Using Sensor Embedded Computing Devices

Islam

2015

International Journal of Distributed Sensor Networks

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Biometric verification has been included in remote user authentication schemes recently. In this paper, we have proposed the use of heartbeat biometrics for its liveness property as a possible defense against spoof attacks in remote authentication. Sensor embedded mobile computing devices such as smart phones could be used to capture biometric signals and to replace the use of traditional smart cards. For the remote authentication, we have adopted the state-of-the-art scheme. We have described the biometric verification method instead of using cryptographic hash function for feature matching. We have used the piecewise aligned morphology (PAM) of heartbeat signal as a biometric feature. The biometric verification method was tested with ECG records of 100 individuals and 1.34% of equal error rate (EER) was obtained. This feature could be computed and transmitted efficiently using mobile computing devices. The proposed scheme offers more security and convenience to users. Additionally, it becomes robust against noisy biometric input. The proposed scheme should be feasible for remote authentication using sensor embedded computing devices in many practical applications such as remote healthcare.

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“…The ECG is difficult to mimic and hard to be copied or stolen. Therefore, the ECG has strong credentials to successfully address the security and privacy issues of an individual [8].…”

Section: Introductionmentioning

confidence: 99%

Identifying Individuals Using Eigenbeat Features of Electrocardiogram

Singh

2013

Journal of Engineering

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The authors of this paper present a new method to characterize the electrocardiogram (ECG) for individual identification. We propose an ECG biometric system which is insensitive to noise signals and muscle flexure. The method utilizes the principal of linearly projecting the heartbeat features into a subspace of lower dimension using an orthogonal basis that represents the most significant features to distinguish the individuals. The performance of the proposed biometric system is evaluated on the subjects of both health statuses such as the ECG recordings of MIT-BIH Arrhythmia database and the ECG recordings of normal subjects prepared at IIT(BHU). The result demonstrates that the derived eigenbeat features from proposed ECG characterization perform better and achieve the recognition accuracy of 91.42% and 95.55% on the subjects of MIT-BIH Arrhythmia database and IIT(BHU) database, respectively.

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Fusion of electrocardiogram with unobtrusive biometrics: An efficient individual authentication system

Cited by 69 publications

References 9 publications

Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Heartbeat Biometrics for Remote Authentication Using Sensor Embedded Computing Devices

Identifying Individuals Using Eigenbeat Features of Electrocardiogram

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