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Breath with nose sound features has been shown as a potential biometric inpersonal identification and verification. In this paper, we show thatinformation that comes from other modalities captured by motion sensors on thechest in addition to audio features could further improve the performance. Ourwork is composed of three main contributions: hardware creation, datasetpublication, and proposed multimodal models. To be more specific, we design newhardware which consists of an acoustic sensor to collect audio features fromthe nose, as well as an accelerometer and gyroscope to collect movement on thechest as a result of an individual's breathing. Using this hardware, we publisha collected dataset from a number of sessions from different volunteers, eachsession includes three common gestures: normal, deep, and strong breathing.Finally, we experiment with two multimodal models based on Convolutional LongShort Term Memory (CNN-LSTM) and Temporal Convolutional Networks (TCN)architectures. The results demonstrate the suitability of our new hardware forboth verification and identification tasks.

Personalized breath based biometric authentication with wearable multimodality