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Open-set Person Identification with Triple-Joint Loss Based on Radar Gait Micro-Doppler Signatures

Open-set person identification based on radar gait micro-Doppler features has attracted great attention and plays an important role in real-time identification and intruder detection. We propose a novel triple-joint loss to achieve open-set person identification. Firstly, the deep learning network is used to map the gait features to the high-dimensional space. The Large Margin Cosine loss is introduced with the traditional Softmax loss and the Center loss, which forms a triple-joint loss to achieve the classification of gait features in the mapped high-dimensional space. Then, the risk area is divided according to the radius corresponding to each class center inclusion probability of 98% for intruder discrimination. Finally, the accuracy of intruder detection in our collection dataset of 11 persons (10 knowns + 1 unknown) can reach 95.5% on average.