Human identification based on radar signatures of individual heartbeats is crucial in various applications, including user authentication in mobile devices, identification of escaped criminals, etc. Usually, optical systems employed to recognize humans are sensitive to ambient light environments, while radar does not have such a drawback, since it has high penetration and all-weather capability. Meanwhile, since micro-Doppler characteristics from the heart of different people are distinct and not easy to fake, it can be used for identification. In this paper, we employed a deep convolutional neural network (DCNN) and conventional supervised learning methods to realize heartbeat-based identification. First, the heartbeat signals were acquired by a Doppler radar and processed by short-time Fourier transform. Then, predefined features were extracted for the conventional supervised learning algorithms, while time–frequency graphs were directly inputted to the DCNN since the network had its own feature extraction part. It is shown that the DCNN could achieve average accuracy of 98.5% for identifying four people, and higher than 80% when the number of people was less than ten. For conventional supervised learning algorithms when identifying four people, the accuracy of the support vector machine (SVM) was 88.75%, and the accuracy of SVM–Bayes was 91.25%, while naive Bayes had the lowest accuracy of 80.75%.

Radar has great potential in military and civilian areas, including automobile anti-collision, battlefield surveillance, etc., due to its high penetration and allweather capability. On the basis of traditional targets detection, targets classification can be realized. In this paper, a comparison of targets classification between deep learning (Deep Convolutional Neural Networks (DCNNs)) and conventional supervised learning methods (Support Vector Machine (SVM), Naive Bayes (NB) and SVM-Bayes fusion algorithm) has been made. Furthermore, several factors affecting the accuracy of classifying targets including SNR, decrease of samples, have been researched and discussed. We employ a K-band Doppler radar to acquire the raw signal due to its stationary clutter-rejection, movement detection ability and short wavelength. Then Shorttime Fourier Transform (STFT) is applied to the raw signal to characterize micro-Doppler signatures which is the fundament of the classification process. We adopt the DCNNs to deal with the spectrograms directly, while features have been designed and extracted for classification with conventional supervised learning methods. It is shown that the DCNN can achieve average accuracy approximately 99.4% followed by SVM-Bayes fusion algorithm reaching around 95.8%, while the accuracy for SVM and NB is about 94.4% and 91% respectively.