eigenPulse: Robust human identification from cardiovascular function

Irvine, John; Israel, Steven A.; Scruggs, William; Worek, William J.

doi:10.1016/j.patcog.2008.04.015

Cited by 114 publications

(71 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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%

See 1 more Smart Citation

Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Singh

Ray

2012

ISRN Signal Processing

View full text Add to dashboard Cite

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.

show abstract

Section: Characteristics Of Bioelectrical Signals As Biometricsmentioning

confidence: 99%

“…In the literature, different methods have been proposed in support of using the ECG signal as a candidate of biometric for identity verification [2][3][4][5][6][7][8][9][10]. Unlike other conventional biometrics (e.g., face and fingerprint), the ECG signal provides the real-time vitality feedback.…”

Section: Supporting Factorsmentioning

confidence: 99%

Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Singh

Ray

2012

ISRN Signal Processing

View full text Add to dashboard Cite

show abstract

“…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%

“…No corresponding enrolment rates or data handling issues were provided by these authors. An eigenPulse method was developed by Israel et al (2003) and refined by Irvine et al (2008). For eigenPulse, the processing follows that commonly used for face recognition (Turk and Pentland, 1991).…”

Section: Feature Extractionmentioning

confidence: 99%

“…The supervised classification articles focused on statistical and neural network approaches (Tatara and Cinar, 2002). Irvine et al (2003) used linear discriminant analysis (LDA) to separate among the individuals. The LDA statistically models the hypersurfaces that best separate the subjects' attributes in the feature space.…”

Section: Algorithms and Decision Rulesmentioning

confidence: 99%

See 1 more Smart Citation

Heartbeat biometrics: a sensing system perspective

Israel¹,

Irvine²

2012

IJCB

Self Cite

View full text Add to dashboard Cite

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.

show abstract

ECG biometric recognition using SVM‐based approach

Rezgui

Lachiri

2016

IEEJ Transactions Elec Engng

View full text Add to dashboard Cite

This paper presents a new approach for biometric personal identification based on electrocardiogram (ECG) features. ECG, which reflects cardiac electrical activity, is a distinctive characteristic of a person and can be used for security needs. Twentyone features based on temporal and amplitude distances between detected fiducial points and 10 morphological descriptors are extracted from each heartbeat. Then, support vector machine (SVM) is used as a classifier. A comparative study between two kernels, Gaussian and polynomial, was made in order to determine the best kernel and the appropriate values of hyperparameters that improve the recognition performance. The algorithm is evaluated using two databases, namely MIT-BIH Arrhythmia and MIT-BIH Normal Sinus Rhythm. Analysis of the results shows that the combination of all features allows improvement of our system efficiency with regard to healthy human subjects and those with arrhythmia.

show abstract

eigenPulse: Robust human identification from cardiovascular function

Cited by 114 publications

References 19 publications

Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Bioelectrical Signals as Emerging Biometrics: Issues and Challenges

Heartbeat biometrics: a sensing system perspective

ECG biometric recognition using SVM‐based approach

Contact Info

Product

Resources

About