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.
Highlights
Biometric identification technology mainly consists of two kinds of identification techniques based on physical characteristics and behavioral characteristics, respectively
When it comes to the issue that the recognition rate will decrease with the increase of the vertical distance between pyroelectric infrared (PIR) sensor node and path, this paper proposes a solution based on decision level fusion
As for the system with single PIR sensor, it shows through the comparison of recognition rate based on different feature extraction algorithms that with the increase of the vertical distance between PIR sensors and walking path, the recognition rate tends to decrease
Summary
Biometric identification technology mainly consists of two kinds of identification techniques based on physical characteristics and behavioral characteristics, respectively. Physical characteristics usually include face, fingerprint, retina, etc.; and, behavioral characteristics include signatures, voice, gait, and so forth. Traditional video systems have been applied to many recognition scenes [1,2]. Such systems, primarily identify persons through facial features which are greatly restricted by many external factors such as lighting, angle and clothes. Primarily identify persons through facial features which are greatly restricted by many external factors such as lighting, angle and clothes They usually have high computational overhead and require huge data throughput
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