Radar-Based Face Recognition: One-Shot Learning Approach

Abstract

In this paper, we propose a method for recognizing human faces by applying one-shot learning to radar sensor data. First, we use a small-sized radar sensor with a center frequency of 61 GHz and accumulate radar signals reflected from different human faces. Different spatial characteristics of each human face are reflected in the signals received through multiple receiving channels. Then, we apply the one-shot learning approach for obtaining effective recognition performance even on small data sets, to distinguish different faces. The one-shot learning method has the advantages for extracting feature information from small labelled samples and adapting to new sample not studied previously. To generate the input of the one-shot learning model, the signals received from the multiple channels are concatenated in parallel. The proposed one-shot learning method based on a Siamese network shows a recognition accuracy of almost 97.6% and demonstrates a better recognition performance than the conventional deep neural networks, when applied to the radar signal data of eight different faces.