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研究生： 吳秉鑫
Wu, Ping-Hsin
論文名稱： 以PPG訊號實現身分辨識系統
Human Identification System Based on PPG Signal
指導教授： 高榮駿
Kao, Jung-Chun
口試委員: 楊舜仁
Yang, Shun-Ren
趙禧綠
Chao, Hsi-Lu
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊工程學系
Computer Science
論文出版年： 2020
畢業學年度： 108
語文別： 英文
論文頁數： 52
中文關鍵詞： 光體積描述法 、生物識別 、生物特徵認證 、生物驗證 、傅立葉轉換方法 、類神經網路
外文關鍵詞： photoplethysmographic, biometric identification, biometric authentication, biometric verification, fourier transform, neural network
相關次數： 點閱：2  下載：0 
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    • 每個人的身體特徵都是獨一無二的，且不易被人盜用，因此近年來，生物辨識 (biometric identification)已經大量應用在生活中，像是人體指紋、臉部、聲音或虹膜等，來辨識使用者身分。光電容積圖 (PPG)隨著生物傳感器 (biosensor)技術的提升，量測到的訊號也越來越精準，再加上只要利用穿戴裝置就能有效率地進行量測，因此利用光電容積圖去實行生物辨識也是未來的趨勢。

    在本篇論文中，我們提出一套能在行動裝置上運行的生物辨識系統架構 (biometric identification system)。除此之外，如何從光電容積圖抓取具有獨特性 (unique)的特徵 (feature)，也是我們在本篇提出來的重點。在我們提出的生物辨識系統架構包含識別 (identification)和驗證 (verification)兩個分類階段，並以深度學習為核心去實作兩個階段的分類器 (classifier)。

    Photoplethysmographic (PPG) signal, also known as PPG signal, can be used as a biometric technique due to its unique form that differs between individuals. Rapid advances in miniatures of PPG sensors and smart bracelets have enabled the proliferation of low-cost, wearable biosensors capable, which makes PPG have great potential to serve as a biometric identification system.

    In this thesis, a wearable human identification system based on PPG signal collected from a wearable sensor on a smart bracelet is introduced. To improve the performance of the human identification system, we utilize a deep neuron network which takes raw data and preprocessed features of collected PPG signal as input. In addition, we propose a human identification architecture to offer biometric identification and biometric verification. The proposed biometric identification scheme has been tested on PPG databases and experimental results show that our method achieves higher recognition accuracy than a set of existing methods.

    Abstract i Contents iii List of Figures vi List of Tables ix Chapter 1 Introduction 1 Chapter 2 Related Work 3 Chapter 3 System Model 5 3.1 System overview 5 3.2 Overview of biometric identification system 5 3.3 Operation of biometric identification system 6 Chapter 4 PPG Based Identification Methodology 7 4.1 Wu's Methodology 7 4.1.1 Preprocessing 8 4.1.2 Format of features 11 4.1.3 Architecture of neural network 12 4.2 Lin's Methodology 13 4.2.1 Preprocessing 14 4.2.2 Feature extraction 16 4.2.3 Architecture of neural network 17 Chapter 5 Biometric identification system 19 5.1 Biometric identification system architecture 19 5.2 Splitting of dataset 23 5.3 Threshold decision 23 Chapter 6 Performance Evaluation 24 6.1 Compared Methods 24 6.1.1 KNN 24 6.1.2 SVM 25 6.1.3 Neural network 25 6.2 Datasets 26 6.2.1 Polar OH1 PPG Dataset: 26 6.2.2 BIDMC PPG Dataset: 27 6.2.3 TBME PPG Dataset: 27 6.3 Performance Results 27 6.3.1 Multi-classification (Identification) 27 6.3.2 Binary classification (Verification) 31 6.3.3 Biometric identification system 37 6.4 Extra work 45 Chapter 7 Conclusion 48 Reference 49

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