Personalized Breath-Based Biometric Authentication With Wearable Multimodality

Breath sounds have been shown as a potential biometric in personal identification and verification. In this article, we show that by combining signals captured by motion sensors on the chest with audio features, we can further improve the performance of these tasks. Our work is composed of three main contributions: the design of a newly created Internet-of-Things (IoT) device, the publication of a novel dataset, and newly proposed multimodal models. Specifically, we first design new hardware that consists of an acoustic sensor to collect audio features from the nose, as well as an accelerometer and gyroscope to collect movement on the chest as a result of an individual’s breathing. Using this hardware, we collect and publish a dataset from a number of sessions with different volunteers, where each session includes three common gestures: normal, deep, and strong breathing. Finally, we experiment with two multimodal models based on convolutional long short term memory (CNN-LSTM) and temporal convolutional networks (TCNs) architectures. The results demonstrate the suitability of our new hardware for both verification and identification tasks.