Evaluation of Electrocardiogram for Biometric Authentication
01 Jan 2012-Journal of Information Security (Scientific Research Publishing)-Vol. 3, Iss: 1, pp 39-48
TL;DR: The EER results of the combined systems prove that the ECG has an excellent source of supplementary information to a multibiometric system, despite it shows moderate performance in a unimodal framework.
Abstract: This paper presents an evaluation of a new biometric electrocardiogram (ECG) for individual authentication. We report the potential of ECG as a biometric and address the research concerns to use ECG-enabled biometric authentication system across a range of conditions. We present a method to delineate ECG waveforms and their end fiducials from each heartbeat. A new authentication strategy is proposed in this work, which uses the delineated features and taking decision for the identity of an individual with respect to the template database on the basis of match scores. Performance of the system is evaluated in a unimodal framework and in the multibiometric framework where ECG is combined with the face biometric and with the fingerprint biometric. The equal error rate (EER) result of the unimodal system is reported to 10.8%, while the EER results of the multibiometric systems are reported to 3.02% and 1.52%, respectively for the systems when ECG combined with the face biometric and ECG combined with the fingerprint biometric. The EER results of the combined systems prove that the ECG has an excellent source of supplementary information to a multibiometric system, despite it shows moderate performance in a unimodal framework. We critically evaluate the concerns involved to use ECG as a biometric for individual authentication such as, the lack of standardization of signal features and the presence of acquisition variations that make the data representation more difficult. In order to determine large scale performance, individuality of ECG remains to be examined.
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TL;DR: To the best of the knowledge, this is the first approach on mobile authentication that uses ECG biometric signals and it shows a promising future for this technology, although further improvements are still needed to optimize accuracy while maintaining a short acquisition time for authentication.
Abstract: Traditional mobile login methods, like numerical or graphical passwords, are vulnerable to passive attacks. It is common for intruders to gain access to personal information of their victims by watching them enter their passwords into their mobile screens from a close proximity. With this in mind, a mobile biometric authentication algorithm based on electrocardiogram (ECG) is proposed. With this algorithm, the user will only need to touch two ECG electrodes (lead I) of the mobile device to gain access. The algorithm was tested with a cell phone case heart monitor in a controlled laboratory experiment at different times and conditions with ten subjects and also with 73 records obtained from the Physionet database. The obtained results reveal that our algorithm has 1.41% false acceptance rate and 81.82% true acceptance rate with 4 s of signal acquisition. To the best of our knowledge, this is the first approach on mobile authentication that uses ECG biometric signals and it shows a promising future for this technology. Nonetheless, further improvements are still needed to optimize accuracy while maintaining a short acquisition time for authentication.
182 citations
Cites background or methods from "Evaluation of Electrocardiogram for..."
...used in other works such as [9], [11], [12], [16], and [17];...
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...Singh and Singh [16] indicate that they used one half of each record for enrollment and the other half for authentication....
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...We ran our algorithm using comparable ECG data to similar studies such as [12], [16], and [17]....
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...In our experiment, we therefore used 73 records from the same Physionet databases as [16] and [17]....
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...Another ECG biometric approach is presented by Singh and Singh [16], where they extract 20 features from each heartbeat, including those based on time, amplitude, and angles....
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04 Oct 2017
TL;DR: It is demonstrated that Cardiac Scan is a robust and usable continuous authentication system based on geometric and non-volitional features of the cardiac motion, which features intrinsic liveness detection, unobtrusiveness, cost-effectiveness, and high usability.
Abstract: Continuous authentication is of great importance to maintain the security level of a system throughout the login session. The goal of this work is to investigate a trustworthy, continuous, and non-contact user authentication approach based on a heart-related biometric that works in a daily-life environment. To this end, we present a novel, continuous authentication system, namely Cardiac Scan, based on geometric and non-volitional features of the cardiac motion. Cardiac motion is an automatic heart deformation caused by self-excitement of the cardiac muscle, which is unique to each user and is difficult (if not impossible) to counterfeit. Cardiac Scan features intrinsic liveness detection, unobtrusiveness, cost-effectiveness, and high usability. We prototype a remote, high-resolution cardiac motion sensing system based on the smart DC-coupled continuous-wave radar. Fiducial-based invariant identity descriptors of cardiac motion are extracted after the radar signal demodulation. We conduct a pilot study with 78 subjects to evaluate Cardiac Scan in accuracy, authentication time, permanence, evaluation in complex conditions, and vulnerability. Specifically, Cardiac Scan achieves 98.61% balanced accuracy (BAC) and 4.42% equal error rate (EER) in a real-world setup. We demonstrate that Cardiac Scan is a robust and usable continuous authentication system.
151 citations
Cites methods from "Evaluation of Electrocardiogram for..."
...The system is also evaluated in combination with face and fingerprint biometrics [75]....
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TL;DR: A deep review and discussion of 93 state-of-the-art publications on their proposed methods, signal datasets, and publicly available ECG collections is conducted to present the fundamentals and the evolution of ECG biometrics, describe the current state of the art, and draw conclusions on prior art approaches and current challenges.
Abstract: Face and fingerprint are, currently, the most thoroughly explored biometric traits, promising reliable recognition in diverse applications. Commercial products using these traits for biometric identification or authentication are increasingly widespread, from smartphones to border control. However, increasingly smart techniques to counterfeit such traits raise the need for traits that are less vulnerable to stealthy trait measurement or spoofing attacks. This has sparked interest on the electrocardiogram (ECG), most commonly associated with medical diagnosis, whose hidden nature and inherent liveness information make it highly resistant to attacks. In the last years, the topic of ECG-based biometrics has quickly evolved toward the commercial applications, mainly by addressing the reduced acceptability and comfort by proposing new off-the-person, wearable, and seamless acquisition settings. Furthermore, researchers have recently started to address the issues of spoofing prevention and data security in ECG biometrics, as well as the potential of deep learning methodologies to enhance the recognition accuracy and robustness. In this paper, we conduct a deep review and discussion of 93 state-of-the-art publications on their proposed methods, signal datasets, and publicly available ECG collections. The extracted knowledge is used to present the fundamentals and the evolution of ECG biometrics, describe the current state of the art, and draw conclusions on prior art approaches and current challenges. With this paper, we aim to delve into the current opportunities as well as inspire and guide future research in ECG biometrics.
131 citations
Cites background or methods from "Evaluation of Electrocardiogram for..."
...The most popular distance metric was, by far, the Euclidean distance [36], [39], [48], [64], [95], [108], [109], [112]....
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...Results of surveyed approaches evaluated with the MIT-BIH Normal Sinus Rhythm database (ordered by number of subjects – NS; works that joined MIT NSR with other databases [8], [28], [58], [88], [89], [93], [112] are not included)....
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...Results of surveyed approaches evaluated with the MIT-BIH Arrhythmia database (ordered by number of subjects – NS; works that joined MIT Arrhythmia with other databases [8], [58], [88], [93], [112] are not included)....
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TL;DR: A survey of the techniques used so far in ECG-based human identification is provided, providing a unifying framework to appreciate previous studies and, hopefully, guide future research.
Abstract: Background: During last decade the use of ECG recordings in biometric recognition studies has increased. ECG characteristics made it suitable for subject identification: it is unique, present in all living individuals, and hard to forge. However, in spite of the great number of approaches found in literature, no agreement exists on the most appropriate methodology. This study aimed at providing a survey of the techniques used so far in ECG-based human identification. Specifically, a pattern recognition perspective is here proposed providing a unifying framework to appreciate previous studies and, hopefully, guide future research. Methods: We searched for papers on the subject from the earliest available date using relevant electronic databases (Medline, IEEEXplore, Scopus, and Web of Knowledge). The following terms were used in different combinations: electrocardiogram, ECG, human identification, biometric, authentication and individual variability. The electronic sources were last searched on 1st March 2015. In our selection we included published research on peer-reviewed journals, books chapters and conferences proceedings. The search was performed for English language documents. Results: 100 pertinent papers were found. Number of subjects involved in the journal studies ranges from 10 to 502, age from 16 to 86, male and female subjects are generally present. Number of analysed leads varies as well as the recording conditions. Identification performance differs widely as well as verification rate. Many studies refer to publicly available databases (Physionet ECG databases repository) while others rely on proprietary recordings making difficult them to compare. As a measure of overall accuracy we computed a weighted average of the identification rate and equal error rate in authentication scenarios. Identification rate resulted equal to 94.95 % while the equal error rate equal to 0.92 %. Conclusions: Biometric recognition is a mature field of research. Nevertheless, the use of physiological signals features, such as the ECG traits, needs further improvements. ECG features have the potential to be used in daily activities such as access control and patient handling as well as in wearable electronics applications. However, some barriers still limit its growth. Further analysis should be addressed on the use of single lead recordings and the study of features which are not dependent on the recording sites (e.g. fingers, hand palms). Moreover, it is expected that new techniques will be developed using fiducials and non-fiducial based features in order to catch the best of both approaches. ECG recognition in pathological subjects is also worth of additional investigations.
124 citations
01 Sep 2013
TL;DR: An evaluation of the permanence of ECG signals collected at the fingers, with respect to the biometric authentication performance, and experimental results on a small dataset suggest that further research is necessary to account for and understand sources of variability found in some subjects.
Abstract: Over the past few years, the evaluation of Electrocardio-graphic (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 intru-siveness, but also from the lack of evidence on the permanence of the ECG templates over time. The problem of in-trusiveness has been recently overcome with the “off-the-person” 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.
100 citations
Cites background from "Evaluation of Electrocardiogram for..."
...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....
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References
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01 Jan 1973
20,541 citations
"Evaluation of Electrocardiogram for..." refers background in this paper
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TL;DR: A real-time algorithm that reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width of ECG signals and automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate.
Abstract: We have developed a real-time algorithm for detection of the QRS complexes of ECG signals. It reliably recognizes QRS complexes based upon digital analyses of slope, amplitude, and width. A special digital bandpass filter reduces false detections caused by the various types of interference present in ECG signals. This filtering permits use of low thresholds, thereby increasing detection sensitivity. The algorithm automatically adjusts thresholds and parameters periodically to adapt to such ECG changes as QRS morphology and heart rate. For the standard 24 h MIT/BIH arrhythmia database, this algorithm correctly detects 99.3 percent of the QRS complexes.
6,686 citations
"Evaluation of Electrocardiogram for..." refers methods in this paper
...The heartbeats from the ECG trace are detected using QRS complex delineator which is implemented using the technique proposed in [10] with some improvements....
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Journal Article•
TL;DR: In this paper, a preliminary attempt was made to determine from blood pressure records the relative influence of the heart action and of vaso-canstriction, and it was suggested that it might be necessary to estimate the duration of ventricular systole for different heart rates.
Abstract: IN a preliminary attempt (which requires considerable modification) to determine from blood-pressure records the relative influence of the heart action and of vaso-canstriction, I suggestedS that it might be necessary to estimate the duration of ventricular systole for different heart rates. In order to obtain this information a number of measurements have been made of electrocardiographic curves, including some obtained by myself and a selection of curves from Dr. T. Lewis’s collection, which he very kindly put at my disposal. Electrical records have been preferred to mechanical, because it is easier to secure accuracy, and it has been shown by many workers that as a rub the electrical and mechanical changes correspond fairly closely. Lewis ,*7 in a comparison of the heart sounds with the electrical changes, found the first sound to commence 0.011 of a second to 0.039 of a second after the commencement of Q, while the second sound started either before or after the end of T but usually within 0.01 of a second of it. WiggersYS1 working with dogs, found the mechanical systole to commence 0.03 to 0.045 after the rise of R, and to terminate 0.034 to 0.048 after the end of T, so that as a rule the two changes corresponded in duration, but he found that adrenalin shortens the duration of the mechanical change more than the electrical, and under these conditions the ventrical contrsction ended before the end of the T wave. In considering, therefore, the relative duration of systole and diastole, both electrical and mechanical records are useful, if these differences be allowed for. Walleflsgivee the following values for the durebtion of mechanical systole with different heart rates, and it will be seen that almost exactly similar figures are obtained by calculation from the formula systole = K Vcycle, where K hae 8 value of 0.343.
4,324 citations
TL;DR: Experiments show that it is possible to identify a person by features extracted from one lead only, and only three electrodes have to be attached on the person to be identified.
Abstract: A new approach in human identification is investigated. For this purpose, a standard 12-lead electrocardiogram (ECG) recorded during rest is used. Selected features extracted from the ECG are used to identify a person in a predetermined group. Multivariate analysis is used for the identification task. Experiments show that it is possible to identify a person by features extracted from one lead only. Hence, only three electrodes have to be attached on the person to be identified. This makes the method applicable without too much effort.
861 citations
"Evaluation of Electrocardiogram for..." refers methods in this paper
...Different methods in support of using ECG as a candidate of biometric have been proposed in the literature [2-8]....
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