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The use of electrocardiogram (ECG) data for personal identification in Industrial Internet of Things can achieve near-perfect accuracy in an ideal condition. However, real-life ECG data are often exposed to various types of noises and interferences. A reliable and enhanced identification method could be achieved by employing additional features from other biometric sources. This work, thus, proposes a novel robust and reliable identification technique grounded on multimodal biometrics, which utilizes deep learning to combine fingerprint, ECG and facial image data, particularly useful for identification and gender classification purposes. The multimodal approach allows the model to deal with a range of input domains removing the requirement of independent training on each modality, and inter-domain correlation can improve the model generalization capability on these tasks. In multitask learning, losses from one …