Volume 13 Number 1 (Jan. 2018)
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JCP 2018 Vol.13(1): 49-57 ISSN: 1796-203X
doi: 10.17706/jcp.13.1.49-57

Classification Accuracy of Personal Identification Based on Joint Motions Using 2D Information

Ryusuke Miyamoto1, Risako Aoki2
1Department of Computer Science, School of Science and Technology, Meiji University, Kanagawa, Japan.
2Department of Fundamental Science and Technology, Graduate School of Science and Technology, Meiji University, Kanagawa, Japan.

Abstract—This paper evaluates the classification accuracy of personal identification by a classification scheme with feature extraction based on joint motions using only two-dimensional information. Experimental results show that the feature extraction based on joint motions can achieve moderate classification accuracy when feature vectors are constructed from only two-dimensional information in an image plane. In addition, the results include interesting knowledge: the classification accuracy is not degraded drastically even if a gait is measured from right in front. In the best case, the classification accuracy becomes 78.95% in the experiment and it is 75.44% in the worst case.

Index Terms—Biometrics, Personal Identification, Gait Analysis,

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Cite: Ryusuke Miyamoto, Risako Aoki, "Classification Accuracy of Personal Identification Based on Joint Motions Using 2D Information," Journal of Computers vol. 13, no. 1, pp. 49-57, 2018.

General Information

ISSN: 1796-203X
Frequency: Monthly (2006-2014); Bimonthly (Since 2015)
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Cherry L. Chen
Abstracting/ Indexing: DBLP, EBSCO, DOAJ, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, CNKI,etc
E-mail: jcp@iap.org
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