Optimal Estimation of Contour Properties by Cross-Validated Regularization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Biometrics, Personal Identification in Networked Society: Personal Identification in Networked Society
A gesture-based authentication scheme for untrusted public terminals
Proceedings of the 17th annual ACM symposium on User interface software and technology
User evaluation of lightweight user authentication with a single tri-axis accelerometer
Proceedings of the 11th International Conference on Human-Computer Interaction with Mobile Devices and Services
uWave: Accelerometer-based personalized gesture recognition and its applications
Pervasive and Mobile Computing
Analysis of pattern recognition techniques for in-air signature biometrics
Pattern Recognition
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This paper proposes a novel method for biometric identification, based on arm swing motions with a template update in order to improve long term stability. In our previous work, we studied arm swing identification and proposed a basic method to realize a personal identification function on mobile terminals. The method compares the acceleration signals of arm swing motion as individual characteristics, with the tolerant similarity measurement between two arm swing motions via DP-matching, which enables users to unlock a mobile terminal simply by swinging it. However, the method has a problem with long term stability. In other words, the arm swing motions of identical individuals tend to fluctuate among every trial. Furthermore, the difference between the enrolled and trial motions increases over time. Therefore in this paper, we propose an update approach to the enrollment template for DPmatching to solve this problem. We employ an efficient adaptive update method using a minimum route determination algorithm in DP-matching. Identification experiments involving 12 persons over 6 weeks confirm the proposed method achieves a superior equal error rate of 4.0% than the conventional method, which has an equal error rate of 14.7%.