IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Journal of Cognitive Neuroscience
Pattern Recognition
Matching pursuit filters applied to face identification
IEEE Transactions on Image Processing
Decision-based neural networks with signal/image classification applications
IEEE Transactions on Neural Networks
Use of random time-intervals (RTIs) generation for biometric verification
Pattern Recognition
A sequential procedure for individual identity verification using ECG
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Laser Doppler vibrometry measures of physiological function: evaluation of biometric capabilities
IEEE Transactions on Information Forensics and Security
Study of human identification by electrocardiogram waveform morph
CCBR'11 Proceedings of the 6th Chinese conference on Biometric recognition
Strengthening a cryptographic system with behavioural biometric
ITIB'12 Proceedings of the Third international conference on Information Technologies in Biomedicine
Privacy in mobile technology for personal healthcare
ACM Computing Surveys (CSUR)
QRS detection-free electrocardiogram biometrics in the reconstructed phase space
Pattern Recognition Letters
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This paper presents eigenPulse, a new method for human identification from cardiovascular function. Traditional biometric techniques, e.g. face and fingerprint, have used eigen analysis to exploit databases with tens of thousands of entries. One drawback of traditional biometrics is that the credentials, for example, fingerprints, can be forged making the systems less secure. Previous research [S.A. Israel, J.M. Irvine, A. Cheng, M.D. Wiederhold, B.K. Wiederhold, ECG to identify individuals, Pattern Recognition 38(1) (2005) 138-142] demonstrated the viability of using cardiovascular function for human identification. By nature, cardiovascular function is a measure of liveness and less susceptible to forgery. However, the classification techniques presented in earlier work performed poorly over non-standard electrocardiogram (ECG) traces, raising questions about the percentage of the population that can be enrolled. This paper combines the traditional biometrics' use of eigen analysis and previous analysis of cardiovascular function to yield a more robust approach. The eigenPulse processing had a near 100% enrollment rate, with a corresponding higher overall performance.