Towards Invariant Soft Biometrics from Electrocardiograms
Medical data such as the electrocardiogram (ECG) has received an increased interest within biometric settings. One of the main benefits is the difficulty in counterfeiting the information due to its hidden nature. However, medical information may be exposed to intra-subject variability. This is evident in extraction of soft biometric traits from ECG, where results can vary widely depending on cardiac condition and status. This work investigates methods of lowering the variability, by employing multi-task learning on a shared feature extractor. Three different architectures are suggested and benchmarked. Specifically, experiments are carried out on age and gender estimation showing state of the art results on two public ECG datasets, while lowering estimation variance among instances.
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