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Algorithms 2014, 7(4), 685-702; doi:10.3390/a7040685

Fusion of Multiple Pyroelectric Characteristics for Human Body Identification

Key Laboratory of Fiber Optical Sensing Technology and Information Processing, Ministry of Education, Hubei Key Laboratory of Broadband Wireless Communication and Sensor Network, Wuhan University of Technology, Wuhan, Hubei 430070, China
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Received: 13 October 2014 / Revised: 8 December 2014 / Accepted: 10 December 2014 / Published: 18 December 2014
(This article belongs to the Special Issue Algorithms for Wireless Sensor Networks)
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Abstract

Due to instability and poor identification ability of single pyroelectric infrared (PIR) detector for human target identification, this paper proposes a new approach to fuse the information collected from multiple PIR sensors for human identification. Firstly, Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT), Wavelet Transform (WT) and Wavelet Packet Transform (WPT) are adopted to extract features of the human body, which can be achieved by single PIR sensor. Then, we apply Principal Component Analysis (PCA) and Support Vector Machine (SVM) to reduce the characteristic dimensions and to classify the human targets, respectively. Finally, Fuzzy Comprehensive Evaluation (FCE) is utilized to fuse recognition results from multiple PIR sensors to finalize human identification. The pyroelectric characteristics under scenarios with different people and/or different paths are analyzed by various experiments, and the recognition results with/without fusion procedure are also shown and compared. The experimental results demonstrate our scheme has improved efficiency for human identification. View Full-Text
Keywords: pyroelectric infrared (PIR) sensor; feature extraction; principal component analysis (PCA); support vector machine (SVM); fuzzy comprehensive evaluation method (FCEM) pyroelectric infrared (PIR) sensor; feature extraction; principal component analysis (PCA); support vector machine (SVM); fuzzy comprehensive evaluation method (FCEM)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Zhou, W.; Xiong, J.; Li, F.; Jiang, N.; Zhao, N. Fusion of Multiple Pyroelectric Characteristics for Human Body Identification. Algorithms 2014, 7, 685-702.

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