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The use of gait micro-Doppler signatures to identify a person is a hot topic of research. In this paper, we present a new CNN-based method called  Multi-Scale CNN  (MS-CNN) to obtain features at multiple scales. It extracts shallow features at low-level multi-scale blocks by using multiple kernels at the same time, then extracts deep features and fuses multi-branch embedding features at high-level multi-branch blocks. Experimental results reveal that our method outperforms other commonly used CNN algorithms in terms of accuracy, allowing it to be used for personal identification.

Person identification using a new CNN-based method and radar gait micro-Doppler signatures