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Personal Authentication Using a Kinect Sensor

Author

Listed:
  • Xuanang Feng

    (Nagoya University)

  • Yi Zuo

    (Nagoya University)

  • Eisuke Kita

    (Nagoya University
    Kobe University)

  • Fumiya Saito

    (Nagoya University)

Abstract

This article proposes a new approach to personal authentication by exploring the features of a person’s face and voice. Microsoft’s Kinect sensor is used for facial and voice recognition. Parts of the face including the eyes, nose, and mouth, etc., are analyzed as position vectors. For voice recognition, a Kinect microphone array is adopted to record personal voices. Mel-frequency cepstrum coefficients, logarithmic power, and related values involved in the analysis of personal voice are also estimated from the voices. Neural networks,support vector machines and principal components analysis are employed and compared for personal authentication. To achieve accurate results, 20 examinees were selected for face and voice data used for training the authentication models. The experimental results show that the best accuracy is achieved when the model is trained by a support vector machine using both facial and voice features.

Suggested Citation

  • Xuanang Feng & Yi Zuo & Eisuke Kita & Fumiya Saito, 2017. "Personal Authentication Using a Kinect Sensor," The Review of Socionetwork Strategies, Springer, vol. 11(2), pages 201-215, December.
    • Handle: RePEc:spr:trosos:v:11:y:2017:i:2:d:10.1007_s12626-017-0010-5
    DOI: 10.1007/s12626-017-0010-5
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    Cited by:

    1. Xuanang Feng & Hiroki Shimokubo & Eisuke Kita, 2018. "Personal Identification Through Pedestrians’ Behavior," The Review of Socionetwork Strategies, Springer, vol. 12(2), pages 237-252, December.

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