Multimodal biometric system combining ECG and sound signals

• A combination of sound and ECG signal as behavioural biometric features is proposed.
• The extracted features represent the human reactions in response to the given stimulations.
• Dimensionality reduction methods have been used to optimise the feature set.
• The Nearest Neighbour classifier yields the highest identification accuracy.

In the paper a novel multimodal, behavioural biometric system that combines ECG and sound signal is described. The signal acquisition has been carried out in a small stress condition when the user has been asked to utter sounds at a given pitch. Thanks to the stimulations the ECG signal and especially the HR vary over time and these changes can be used to extract biometric features which represent an individual response to the stimuli. The proposed approach utilised popular statistical coefficients which are computationally effective and simple.