光学精密工程2011，Vol.19Issue(4)：884-891,8.DOI:10.3788/OPE.20111904.0884
基于特征子模式典型相关分析的热释电红外信号识别
Pyroelectric infrared signal recognition based on feature sub-pattern canonical correlation analysis
摘要
Abstract
To improve the recognition ability of a pyroelectric infrared (PIR) detector for different infrared radiation sources, a method for human and non-human recognition based on Canonical Correlation Analysis (CCA) was proposed. Firstly, the frequency spectrum and wavelet packet entropy were extracted as features,and the spectrum was divided into sub-patterns. Then, each sub-pattern and wavelet packet entropy were fused with CCA method, and the fused feature was employed as classification information. By this way, the feature fusion was realized and the redundant information among the features was also eliminated. Finally, the recognition results were obtained by a majority voting method. As a special case of the sub-pattern fusion, the classification abilities of the features fused with their own sub-pattern were also studied in the paper. Experimental results show when the fre quency is divided into 5 sub - patterns , the recognition rate can reach 95. 2％, which is higher 2. 7％ than that of only fusing the frequency and the wavelet packet entropy. Moreover, the recognition rate of wavelet packet entropy fused with its own sub-pattern is 90.7％ , which is higher 2.3％ than that of wavelet packet entropy.关键词
热释电红外(PIR)探测器/小波包熵/子模式典型相关分析(CCA)/特征融合Key words
Pyroelectric Infrared (PIR) detector/ wavelet packet entropy/ sub-pattern Canonical Correlation Analysis (CCA)/ feature fusion分类
信息技术与安全科学引用本文复制引用
龚卫国,王林泓,贺莉芳..基于特征子模式典型相关分析的热释电红外信号识别[J].光学精密工程,2011,19(4):884-891,8.基金项目
公安部应用创新项目(No.2010YYCXCQSJ074) （No.2010YYCXCQSJ074）
国防基础研究基金资助项目(No.CS-OC2) （No.CS-OC2）
重庆市重大科技攻关项目(No.CSTC2009AB0175) （No.CSTC2009AB0175）
