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

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Abstract

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.

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Funding

The work leading to these results has been supported by AMIC (MINECO, TIN2017-85854-C4-4-R), and CAVIAR (MINECO, TEC2017-84593-C2-1-R) projects partially funded by the European Union. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan X Pascal GPU used for this research.

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Authors and Affiliations

  1. Speech Technology Group, Information Processing and Telecomunications Center, E.T.S.I. Telecomunicación, Universidad Politécnica de Madrid, Ciudad Universitaria, SN, 28040, Madrid, Spain

    Manuel Gil-Martín, Rubén San-Segundo, Ricardo de Córdoba & José Manuel Pardo

Authors
  1. Manuel Gil-Martín
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  2. Rubén San-Segundo
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  3. Ricardo de Córdoba
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  4. José Manuel Pardo
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Contributions

Manuel Gil-Martín: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Roles/Writing - original draft. Rubén San-Segundo: Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Writing - review & editing. Ricardo de Córdoba: Conceptualization, Funding acquisition, Project administration, Supervision, Validation, Writing - review & editing. José Manuel Pardo: Conceptualization, Funding acquisition, Project administration, Supervision, Validation, Writing - review & editing.

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Correspondence to Manuel Gil-Martín.

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Gil-Martín, M., San-Segundo, R., de Córdoba, R. et al. Robust Biometrics from Motion Wearable Sensors Using a D-vector Approach. Neural Process Lett 52, 2109–2125 (2020). https://doi.org/10.1007/s11063-020-10339-z

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