A syntactic method for analysis of saccadic eye movements
Pattern Recognition
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Computer Methods and Programs in Biomedicine
Verification of humans using the electrocardiogram
Pattern Recognition Letters
Palmprint Verification for Controlling Access to Shared Computing Resources
IEEE Pervasive Computing
Analysis of human electrocardiogram for biometric recognition
EURASIP Journal on Advances in Signal Processing
Behavioural biometrics: a survey and classification
International Journal of Biometrics
A user authentication based on human reflexes using blind spot and saccade response
International Journal of Biometrics
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
IEEE Transactions on Information Technology in Biomedicine - Special section on biomedical informatics
Biometric identification via an oculomotor plant mathematical model
Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications
Pattern Recognition
Journal of Network and Computer Applications
Biometric recognition: how do i know who you are?
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Using the Timing Information of Heartbeats as an Entity Identifier to Secure Body Sensor Network
IEEE Transactions on Information Technology in Biomedicine
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Matching digital fingerprint, face or iris images, biometric verification of persons has advanced. Notwithstanding the progress, this is no easy computational task because of great numbers of complicated data. Since the 1990s, eye movements previously only applied to various tests of medicine and psychology are also studied for the purpose of computer interfaces. Such a short one-dimensional measurement signal contains less data than images and may therefore be simpler and faster to recognize. Using saccadic eye movements we developed a computational verification method to reliably distinguish a legitimate person or a subject in general from others. We tested features extracted from signals recorded from saccade eye movements. We used saccades of 19 healthy subjects and 21 otoneurological patients recorded with electro-oculography and additional 40 healthy subjects recorded with a videocamera system. Verification tests produced high accuracies.