Deep Learning-based Open-set Person Identification using Radar Extracted Cardiac Signals

Person identification based on radar-extracted vital signs has become increasingly popular due to its non-contact measurement capabilities. This paper introduces a novel deep learning-based person identification algorithm leveraging radar- extracted vital signs. While current studies mainly focus on closeset conditions with consistent training and testing categories, real-world scenarios often involve open-set circumstances, in which there are more data categories in the testing data. The algorithm involves extracting heart pulse signals from Doppler radar echoes, training two Convolutional Neural Network (CNN)-based models using transfer learning, and utilizing a distribution model for calibration. By combining the models' outputs through a strategic decision-making process, we achieve superior person identification results. Experimental results on a public radar vital signs dataset demonstrate an identification accuracy of 99.61% in close-set conditions and 94.35% in openset conditions, surpassing existing approaches.