本研究的目的是解釋構建家庭安全面部識別所需的不同組件。此外，本文還解釋了人臉識別中常用的不同技術，包括識別算法、傳感器和方法論。同時，我們也會討論系統運行中無效的限制。通過對人臉識別相關論文的廣泛閱讀和整理，我們給出評價意見，為未來感興趣的研究人員提供清晰的研究背景。這篇論文的報告還表明，越來越多的深度學習技術被用於家庭安全的人臉識別的不同主題。

The objective of this paper is to compile information necessary to mark up a facial recognition-based home security by exploring the different components in it. This thesis also aims to show the different methods used for facial recognition by identifying the common techniques used such as algorithms, sensors, and methodologies. Limitations will also be discussed to show the downside of these types of systems. Because of extensive research reading facial recognition papers, we can critique the information used in these papers and synthesize this information to construct a path that other researchers will be able to use in the future. The results of this survey paper have shown that there more uses for more deep learning techniques to solve some of the issues that occur in facial recognition-based home security.

Acknowledgement i
Abstract ii
摘要 iii
Table of Contents iv
List of Figures vii
List of Tables ix
List of Abbreviations x
Glossary xi
Chapter 1: Introduction 1
1.1 Motivation 1
1.2 Development of Facial recognition-based home security systems 1
1.3 Contribution 2
1.4 Organization of the thesis 4
Chapter 2: History 5
2.1 Historical view of facial recognition 5
2.2 Historical view of Home Security 8
2.3 Integration of the two technologies 9
2.4 Benefits of Facial recognition in HS 10
2.5 Structure of FRBHS 11
Chapter 3: Core steps of Facial recognition and Algorithms used in FRBHS 16
3.1 Core step of facial recognition in HS 16
3.1.1 Motion Sensor module 17
3.1.2 Face Detection 19
3.1.2.1. Preprocessing 19
3.1.2.2 Detection 20
3.1.3 Facial feature extraction 21
3.1.3.1 Generic Methods 21
3.1.3.2 Feature based template 21
3.1.3.3 Appearance-based template 23
3.1.4 How are feature are represented? 23
3.1.5 Classifications 24
3.2 Common FR Algorithms used in HS 26
3.2.1 Haar-Cascade features 26
3.2.2 MTCNN 29
3.2.3 Face detection algorithms comparisons 31
3.2.4 Facial Recognition algorithms 32
3.2.4.1 LBPH 32
3.2.4.2 FaceNet 35
3.2.4.3 Comparison and Compatibility 38
3.3 Synopsis of all facial recognition algorithms and classifiers 39
Chapter 4. Sensors and Sensors Data 43
4.1 Image-Related Sensor data 44
4.2 Non-Image-Related Sensor data 49
4.3 Sensor conditions 51
Chapter 5. Single-boards 52
5.1 The main functions of Single-boards 52
5.1.1 Sensor processing 53
5.1.2 Face identification 55
5.1.2.1 The single board workflow 56
5.1.3 Memory and facial recognition on a single board 58
5.2 Face Verification: Training 61
5.2.1 Image collection 61
5.2.2 Initializing, face extraction, compression and training 65
Chapter 6. Current Problems, Future Solutions, Conclusion and Future works 70
6.1 Speed and USB accelerators 70
6.2 Accessories, false-motion reading, mask-oriented dataset and object detection 70
6.3 Training data and one sample training 73
6.4 Conclusion 73
6.5 Future work 75
References 77

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