  Obtaining a smart surveillance requires a sensing system that can capture
accurate and detailed information for the human walking style. The radar
micro-Doppler ($\boldsymbol{\mu}$-D) analysis is proved to be a reliable metric
for studying human locomotions. Thus, $\boldsymbol{\mu}$-D signatures can be
used to identify humans based on their walking styles. Additionally, the
signatures contain information about the radar cross section (RCS) of the
moving subject. This paper investigates the effect of human body
characteristics on human identification based on their $\boldsymbol{\mu}$-D
signatures. In our proposed experimental setup, a treadmill is used to collect
$\boldsymbol{\mu}$-D signatures of 22 subjects with different genders and body
characteristics. Convolutional autoencoders (CAE) are then used to extract the
latent space representation from the $\boldsymbol{\mu}$-D signatures. It is
then interpreted in two dimensions using t-distributed stochastic neighbor
embedding (t-SNE). Our study shows that the body mass index (BMI) has a
correlation with the $\boldsymbol{\mu}$-D signature of the walking subject. A
50-layer deep residual network is then trained to identify the walking subject
based on the $\boldsymbol{\mu}$-D signature. We achieve an accuracy of 98% on
the test set with high signal-to-noise-ratio (SNR) and 84% in case of different
SNR levels.
