# Fusion of Multiple Pyroelectric Characteristics for Human Body Identification

^{*}

## Abstract

**:**

## 1. Introduction

- Reductions both in the number of measurements and in sampling frequency for human motion state estimation.
- Reductions in hardware cost, power consumption, privacy, infringement, computational complexity, communication overhead, and networking data throughput.
- Reductions in system deployment duration, limitations upon applications or application location restrictions (e.g., long range or crowded scene).
- Its performance is independent of illumination and has strong robustness to the color change of background.
- Its sensitivity range of angular rate is about 0.1 r/s to 3 r/s [5,6], which can cover the most human walking speeds at around 2–10 m. It can obtain better field of view (FOV) combined with low price Fresnel lens array. Thus, compared with the traditional video systems, distributed wireless pyroelectric sensor networks can provide better spatial coverage and reduce the deployment duration and deployment location restrictions.

- Different Fresnel lens and signal modulation mask can obtain more pyroelectric infrared information of the human target.
- The four sensors are installed with different heights, which can collect different pyroelectric infrared information from corresponding parts of the human body.
- The effective data is fused by multiple channel signals which are collected from the four sensor modules.
- Extracting different pyroelectric infrared features of the human target by different algorithms can help establish different target identification model databases.

## 2. Related Work

## 3. Sensor Modules and Deployment

#### 3.1. PIR Sensor Module

_{3}film pyroelectric infrared sensor is chosen as the detecting node in the system. Due to the lower receiving sensitivity of the sensor itself, each signal sensor node is covered by a Fresnel lens as shown in Figure 1. It cannot only focus the infrared heat to the sensor node, but also can increase the angle and detectable distance. It was proved in some experiments that the effective detectable range can vary from 2 to 12 or 14 m. Based on the parameters and characteristics of the D205B sensor as shown in Table 1, the PIR sensor module is designed as shown in Figure 1.

**Figure 1.**A pyroelectric infrared sensor module is covered by Fresnel lens and signal modulation mask.

Parameters | Value |
---|---|

IR Receiving Electrode | 0.7 × 2.4 mm, 4 elements |

Sensitivity | ≥4300 V/W |

Detectivity (D*) | 1.6 × 108 cm (Hz)^{1/2}/W |

Supply Voltage | 3–15 V |

Operating Temp | −30–70 °C |

Offset Voltage | 0.3–1.2 V |

FOV | 150° |

#### 3.2. PIR Sensor Node

#### 3.3. Gateway Module

## 4. Target Recognition System

#### 4.1. System Architecture

#### 4.2. Experimental Program

Object | Sexuality | Height | Step distance |
---|---|---|---|

a | male | 175 cm | 50 cm |

b | male | 167 cm | 40 cm |

c | female | 160 cm | 40 cm |

d | male | 182 cm | 60 cm |

e | male | 172 cm | 50 cm |

f | female | 158 cm | 37 cm |

g | female | 162 cm | 40 cm |

h | male | 179 cm | 56 cm |

i | male | 173 cm | 50 cm |

j | female | 171 cm | 52 cm |

## 5. Algorithm Descriptions

#### 5.1. Feature Extraction

#### 5.1.1. FFT + PCA (Fast Fourier Transform and Principal Component Analysis)

_{1}, x

_{2}, x

_{3}, …, x

_{n}]

^{T}, x

_{1:}n is a row vector of p dimension which means the spectrum characteristics of each sample, where p is the number of the spectrum points. PCA algorithm are briefly described as below [27]:

- Standardize the observation matrix X to obtain matrix Y;
- Calculate Z which is the covariance of matrix Y;

_{1}≥ λ

_{2}≥ λ

_{3}≥ … ≥ λ

_{p}. The corresponding eigenvectors are U

_{1}, U

_{2}, U

_{3}, …, U

_{p}, and covariance matrix Z can be expressed as:

#### 5.1.2. STFT (Short-Time Fourier Transform)

_{m}

_{× n}is the sample matrix and there are orthogonal matrix U

_{m}

_{× n}and V

_{m}

_{× n}.

_{1},…,σ

_{r}), σ

_{1}≥ … ≥ σ

_{r}is the singular value of A.

#### 5.1.3. WT (Wavelet Transform)

#### 5.1.4. WPT (Wavelet Packet Transform)

#### 5.2. Feature Fusion and Recognition

#### 5.2.1. FCEM (Fuzzy Comprehensive Evaluation Method)

#### 5.2.2. SVM (Support Vector Machine)

_{i})is a kernel function and a

_{i}is Lagrange multiplier. In this paper, k-fold cross validation is used to optimize classification results of SVM. Its basic idea is that original training data is divided into training data and test data, where (k − 1)n/k elements are selected from $n$ sample as the training data and the left n/k acts as the test data to train SVM classifier, which is repeated for M times. Then, we can obtain the recognition rate for an average of M times, and get the final average recognition rate.

## 6. Experiments and Results Analysis

#### 6.1. Feature Extraction

**Figure 9.**The figure is the time domain waveform for target A and target B, where the blue solid line represents the target A; the black dotted line represents the target B; the abscissa represents time; the ordinate represents voltage; each column represents four sensors in different heights; each row represents six kinds of distances.

**Figure 10.**Feature extraction by different algorithms. (

**a**) Feature extraction by FFT. (

**b**) Feature extraction by STFT. (

**c**) Feature extraction by WT. (

**d**) Feature extraction by WPT.

#### 6.2. Comparison of Different Algorithms’ Recognition Rates

**Figure 11.**Recognition results under different algorithms. (

**a**) Recognition results under FFT algorithm. (

**b**) Recognition results under STFT algorithm. (

**c**) Recognition results under WT algorithm. (

**d**) Recognition results under WPT algorithm.

Algorithm | Recognition rate in different path (%) | Computing time(s) | |||||
---|---|---|---|---|---|---|---|

Path1 | Path2 | Path3 | Path4 | Path5 | Path6 | ||

FFT + PCA + SVM + FCE | 83.5 | 89.6 | 76.3 | 86.0 | 77.7 | 65.0 | 10.1 |

STFT + SVM + FCE | 87.6 | 95.5 | 80.5 | 93.5 | 68.0 | 54.5 | 262.8 |

WT + SVM + FCE | 98.7 | 100 | 87.5 | 99.1 | 99.1 | 88.3 | 56.2 |

WPT + SVM + FCE | 95.4 | 97.5 | 90.4 | 97.9 | 95.4 | 90.8 | 25.0 |

## 7. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

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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.
https://doi.org/10.3390/a7040685

**AMA Style**

Zhou W, Xiong J, Li F, Jiang N, Zhao N.
Fusion of Multiple Pyroelectric Characteristics for Human Body Identification. *Algorithms*. 2014; 7(4):685-702.
https://doi.org/10.3390/a7040685

**Chicago/Turabian Style**

Zhou, Wanchun, Ji Xiong, Fangmin Li, Na Jiang, and Ning Zhao.
2014. "Fusion of Multiple Pyroelectric Characteristics for Human Body Identification" *Algorithms* 7, no. 4: 685-702.
https://doi.org/10.3390/a7040685