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 Home > RESEARCH > Research Area > Bio-signal based human authentication
    Bio-signal based human authentication system (updated 2019)

 

 Due to the great advances in biomedical digital signal processing and biomedical instrumentation, new biometric traits have showed noticeable improvements in human identification systems. Commercial biometric authentication systems currently include finger-print, voice, face, and gait recognition. However, personal authentication techniques using fingerprints, irises, veins, etc. have problems of easy forgery

Personal authentication technology using bio-signals such as electrocardiogram (ECG) and electroencephalogram (EEG) is expected to provide new authentication methodologies that are robust to duplication or modulation


1. ECG based person identification

l  The cardiovascular system offers a variety of physiological signals that can be used as biometrics

l  While modality such as the ECG is still relatively novel, it is increasingly garnering acceptance as a useful biometric tool, due to some unique characteristics

l  Each person has slightly different ECG waveforms. It also shows a changing waveform within the same person

l  Novel signal processing algorithms are being studied that maximize inter-variability and minimize intra-variability


2. EEG based person identification

l  The neuronal circuit in the brain has a unique connection form that varies from person to person

l  Electroencephalogram (EEG) also shows a unique pattern for each person during the EEG task

l  Convolutional neural network (CNN) and other deep learning methods can be used to compare and analyze EEG spectrums (image) to find unique patterns for each person


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