Personal Authentication Using a Kinect Sensor

Xuanang Feng, Yi Zuo (), Eisuke Kita () and Fumiya Saito
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Xuanang Feng: Nagoya University
Yi Zuo: Nagoya University
Eisuke Kita: Nagoya University
Fumiya Saito: Nagoya University

The Review of Socionetwork Strategies, 2017, vol. 11, issue 2, 201-215

Abstract: 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.

Keywords: Personal authentication; Kinect; Neural network; Support vector machine; Principal components analysis (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s12626-017-0010-5

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