Evaluation of Electrocardiogram for Biometric Authentication

Singh, Yatindra Nath; Singh, Sanjay Kumar

doi:10.4236/jis.2012.31005

Cited by 81 publications

(40 citation statements)

References 11 publications

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“…In the literature there are several approaches focusing on extraction of features that are invariant to the heart rate, or that try to normalize heartbeats [21,22,23], which is also the topic of ongoing work in our group. In this paper, we focused on the use of latency and amplitude features, in which the template morphing is directly expressed.…”

Section: Discussionmentioning

confidence: 99%

Finger ECG signal for user authentication: Usability and performance

Silva

Fred

Lourenço

et al. 2013

2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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Over the past few years, the evaluation of Electrocardiographic (ECG) signals as a prospective biometric modality has revealed promising results. Given the vital and continuous nature of this information source, ECG signals offer several advantages to the field of biometrics; yet, several challenges currently prevent the ECG from being adopted as a biometric modality in operational settings. These arise partially due to ECG signal's clinical tradition and intrusiveness, but also from the lack of evidence on the permanence of the ECG templates over time. The problem of intrusiveness has been recently overcome with the "off-theperson" approach for capturing ECG signals. In this paper we provide an evaluation of the permanence of ECG signals collected at the fingers, with respect to the biometric authentication performance. Our experimental results on a small dataset suggest that further research is necessary to account for and understand sources of variability found in some subjects. Despite these limitations, "off-the-person" ECG appears to be a viable trait for multi-biometric or standalone biometrics, low user throughput, real-world scenarios.

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Section: Discussionmentioning

confidence: 99%

Finger ECG signal for user authentication: Usability and performance

Silva

Fred

Lourenço

et al. 2013

2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

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“…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%

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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“…They computed the correlation coefficient between such two feature vectors to authenticate a person. Similar to [5], geometrical and/or statistical features from ECG wave (collected from ECG QRS complex) were also used in [8]- [10]. Noureddine et al employed the Discrete Wavelet Transformation (DWT) to extract features from ECG wave and used a Random Forest approach for authentication [11].…”

Section: Introductionmentioning

confidence: 99%