Biometric recognition using wearable devices in real-life settings

• Published in: Pattern recognition letters Vol. 146; pp. 260 - 266
• Main Authors: Piciucco, Emanuela, Di Lascio, Elena, Maiorana, Emanuele, Santini, Silvia, Campisi, Patrizio
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.06.2021; Elsevier Science Ltd
• Subjects: Access control; Artificial neural networks; Biometric recognition systems; Biometrics; Blood volume; Electronic devices; Eyewear; Machine learning; Neural networks; Physical activity; Spectrograms; Wearable; Wearable computers; Wearable technology; Biometrics; Machine learning; 41A10; 65D05; 65D17; Wearable; 41A05
• ISSN: 0167-8655; 1872-7344
• DOI: 10.1016/j.patrec.2021.03.020

•A biometric system based on biosignals captured through a wristband is proposed.•The discriminability and permanence issues of the captured biosignals are addressed.•Shallow and deep-learning classifiers are used to classify time-frequency features.•An average CIR of 98.58% is achieved when recordings of 30s are considered using CNN. The popularity of wearable devices, such as smart glasses, chestbands, and wristbands, is nowadays rapidly growing, thanks to the fact that they can be used to track physical activity and monitor users’ health. Recently, researchers have proposed to exploit their capability to collect physiological signals for enabling automatic user recognition. Wearable devices inherently provide the means for detecting their unauthorized usage, or for being used as front-end in biometric recognition systems controlling the access to either physical or virtual locations and services. The present work evaluates the feasibility of performing biometric recognition using signals captured by wearable devices, considering data collected through off-the-shelf commercial wristbands, and comparing recordings taken during two distinct sessions separated by an average time of 7 days. In more detail, recognition is performed leveraging on electrodermal activity (EDA) and blood volume pulse (BVP), considering measurements taken from 17 subjects performing natural activities such as attending or teaching lectures. Several tests have been carried out to determine the most effective representation of the considered EDA and BVP signals, as well as the most suitable classifier. The best recognition performance has been achieved exploiting convolutional neural networks to extract discriminative characteristics from the combined spectrograms of the employed EDA and BVP data, guaranteeing average correct identification rate of 98.58% for test samples lasting 30 seconds.