  This study proposes a method for radar-based identification of individuals
using a combination of their respiratory and heartbeat features. In the
proposed method, the target individual's respiratory features are extracted
using the modified raised-cosine-waveform model and their heartbeat features
are extracted using the mel-frequency cepstral analysis technique. To identify
a suitable combination of features and a classifier, we compare the
performances of nine methods based on various combinations of three feature
vectors with three classifiers. The accuracy of the proposed method in
performing individual identification is evaluated using a 79-GHz
millimeter-wave radar system with an antenna array in two experimental
scenarios and we demonstrate the importance of use of the combination of the
respiratory and heartbeat features in achieving accurate identification of
individuals. The proposed method achieves accuracy of 96.33% when applied to a
five-day dataset of six participants and 99.39% when applied to a public
one-day dataset of thirty participants.
