Volume 9 Number 9 (Sep. 2014)
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JCP 2014 Vol.9(9): 2035-2042 ISSN: 1796-203X
doi: 10.4304/jcp.9.9.2035-2042

Identity Recognition of Multi-Haptic Pressure Feature based on Sparse Representation

Yan Zhang1, Dong Liang2, Ming Zhu3, and Wenxia Bao3
1Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Physics and Materials Science, Anhui University, Hefei, Anhui 230039, China
2School of Electronics and Information Engineering, Anhui University, Hefei, Anhui 230039, China
3Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electronics and Information Engineering, Anhui University, Hefei, Anhui 230039, China


Abstract—Herein, a new identity recognition method of multi-haptic pressure feature based on sparse representation was investigated. According to the common dynamic features, the regional feature and the ratio of length vs. width of external bounding rectangle (extracted by using the least area method) were extracted. The subset of dynamic feature was optimized by correlation criterion, the sparse representation of haptic pressure was obtained according to the sparse basis (i.e., wavelet basis), and the sparse feature vector was calculated by the Topelitz measurement matrix. After that, the haptic pressure feature set was created by combining dynamic feature subset and sparse feature subset linearly. Furthermore, Support Vector Machine (SVM) classifier identified more than two objects following the one to many rule and output the identification result according to the rule of majority voting, and the stability of features is studied by calculating the intraclass correlation coefficient (ICC) and coefficient of variation (C.V). Over all, the improved accuracy of identity recognition demonstrating the effectiveness and stability of the multi-haptic pressure feature.

Index Terms—Dynamic feature, sparse representation, optimization, correlation criterion, recognition.

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Cite: Yan Zhang, Dong Liang, Ming Zhu, and Wenxia Bao, "Identity Recognition of Multi-Haptic Pressure Feature based on Sparse Representation," Journal of Computers vol. 9, no. 9, pp. 2035-2042, 2014.

General Information

ISSN: 1796-203X
Abbreviated Title: J.Comput.
Frequency: Bimonthly
Editor-in-Chief: Prof. Liansheng Tan
Executive Editor: Ms. Nina Lee
Abstracting/ Indexing: DBLP, EBSCO,  ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat,etc
E-mail: jcp@iap.org
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