ECG to identify individuals

Israel, Steven A.; Irvine, John; Cheng, A.; Wiederhold, Mark D.; Wiederhold, Brenda K.

doi:10.1016/j.patcog.2004.05.014

Cited by 574 publications

(444 citation statements)

References 8 publications

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“…Bioelectrical signals especially, the ECG and the EEG are emerging biometric identities [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Unlike anatomical biometric identities that have two-dimensional data representation, the ECG or EEG is physiologically low-frequency signals that have one-dimensional data representation.…”

Section: Characteristics Of Bioelectrical Signals As Biometricsmentioning

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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Section: Characteristics Of Bioelectrical Signals As Biometricsmentioning

confidence: 99%

Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Singh

Ray

2012

ISRN Signal Processing

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“…Excitement or arousal from any number of stimuli can elevate the heart rate. Irvine et al (2001) and Israel et al (2005) developed an experimental protocol where subjects performed a series of tasks designed to elicit varying mental and emotional states (Irvine et al, 2001;Irvine et al, 2002;Irvine et al, 2003;Israel et al, 2005). The subjects exhibited changes in heart rate associated with these tasks.…”

Section: Sources Of Variancementioning

confidence: 99%

“…The 29 subjects performed seven tasks in the experimental protocol eliciting a range of stimulation (Irvine et al, 2001;Israel et al, 2005). The MANOVA (Figure 11) shows that there are small, but statistically significant, differences in the fiducials across the various tasks, indicating that there are subtle differences in the ECG signal that are more complex than a linear rescaling.…”

Section: Sources Of Variancementioning

confidence: 99%

“…Israel et al (2005) collected data across multiple levels of mental and emotional stimulation. The assay was organised to observe each subject within each session through meditative, stressor, and recovery periods.…”

Section: Experimental Datamentioning

confidence: 99%

“…Within a domain, features are extracted as either raw (Israel et al, 2005), texture (Porta et al, 2001), power spectrum (Barros and Ohnishi, 2001;Stridh et al, 2004), and PCA/ ICA (Garcia et al, 1998;Barros et al, 2000). Afonso et al (1999) integrated the noise reduction and feature extraction by using filterbanks.…”

Section: Feature Extractionmentioning

confidence: 99%

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Heartbeat biometrics: a sensing system perspective

Israel¹,

Irvine²

2012

IJCB

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This paper reviews the emerging research into exploitation of heartbeat data as a biometric for human identification. A variety of methods have been proposed for acquiring heartbeat signatures and a range of processing methods has been examined. We approach the biometric identification and verification problem by characterising the three major factors affecting performance: individual variants, environmental variants, and sensor variants. The ability to collect and process the signal, exploit the data for individual identification or verification, and disseminate the information depends on all three of these factors. Within each component, we have identified the relevant research. Where possible, we have tied these research papers to practical examples using high resolution ECG data. The research indicates that the heartbeat contains rich information about the individual, their level of anxiety, and the cardiac state.Keywords: biometrics; heartbeat; ECG; sensing system; cardiovascular function; human identification. areas of interest include novel biometrics, signal and image processing, quantification and modelling of data quality, and cognitive modeling. He was a Principal Investigator for DARPA's Human Identification at a Distance Program (HumanID). He serves on planning committees for IEEE and SPIE, and has served on several advisory panels for the Department of Defence and the Department of Energy. He has authored over a hundred journal and conference papers and holds a PhD in Mathematical Statistics from Yale University.

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Multimodal Physiological Biometrics Authentication

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ECG to identify individuals

Cited by 574 publications

References 8 publications

Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Heartbeat biometrics: a sensing system perspective

Multimodal Physiological Biometrics Authentication

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