Robust Biometrics from Motion Wearable Sensors Using a D-vector Approach

This paper proposes a d-vector approach for extracting robust biometrics from inertial signals recorded with wearable sensors. The d-vector approach generates identity representations using a deep learning architecture composed of Convolutional Neural Networks. This architecture includes two convolu...

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Bibliographic Details
Published in:Neural processing letters 2020-12, Vol.52 (3), p.2109-2125
Main Authors: Gil-Martín, Manuel, San-Segundo, Rubén, de Córdoba, Ricardo, Pardo, José Manuel
Format: Article
Language:English
Subjects:
Accelerometers
Accuracy
Algorithms
Artificial Intelligence
Artificial neural networks
Biometrics
Boundaries
Complex Systems
Computational Intelligence
Computer architecture
Computer Science
Data mining
Datasets
Deep learning
Gait
Identification
Laboratories
Machine learning
Neural networks
Robustness
Sensors
Smartphones
Supervision
Support vector machines
Verification
Wearable computers
Wearable technology
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Summary:This paper proposes a d-vector approach for extracting robust biometrics from inertial signals recorded with wearable sensors. The d-vector approach generates identity representations using a deep learning architecture composed of Convolutional Neural Networks. This architecture includes two convolutional layers for learning features from the inertial signal spectrum. These layers were pretrained using data from 154 subjects. After that, additional fully connected layers were attached to perform user identification and verification, considering 36 new subjects. This paper compares the proposed d-vector approach with previous proposed algorithms using in-the-wild recordings in different scenarios. The results demonstrated the robustness of the proposed d-vector approach for in-the-wild conditions: 97.69% and 94.16% accuracies (for user identification) and 99.89% and 99.67% Areas Under the Curve (for user verification) were obtained using one (walking) or several activities (walking, jogging and stairs) respectively. These results were also verified in laboratory conditions improving the performance reported in previous works. All the analyses were carried out using public datasets recorded at the Wireless Sensor Data Mining laboratory.
ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-020-10339-z