Personalized breath based biometric authentication with wearable multimodality

TL;DR

This paper introduces a multimodal biometric authentication system using breath and chest movement data captured by custom hardware, demonstrating improved verification and identification performance.

Contribution

It presents a novel hardware setup, a new dataset, and multimodal models combining audio and motion sensors for breath-based biometric authentication.

Findings

Hardware effectively captures breath and movement data.

Multimodal models outperform unimodal approaches.

System suitable for verification and identification tasks.

Abstract

Breath with nose sound features has been shown as a potential biometric in personal identification and verification. In this paper, we show that information that comes from other modalities captured by motion sensors on the chest in addition to audio features could further improve the performance. Our work is composed of three main contributions: hardware creation, dataset publication, and proposed multimodal models. To be more specific, we design new hardware which 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 publish a collected dataset from a number of sessions from different volunteers, 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 (TCN) architectures. The results demonstrate the suitability of our new hardware for both verification and identification tasks.