An Integrated Approach to Home Security and Safety Systems

Length: 190 pages
Edition: 1
Language: English
Publisher: CRC Press
Publication Date: 2021-10-15
ISBN-10: 036763841X
ISBN-13: 9780367638412
Sales Rank: #0 (See Top 100 Books)

This book provides an integrated solution for security and safety in the home, covering both assistance in health monitoring and safety from strangers/intruders who want to enter the home with harmful intentions. It defines a system whereby recognition of a person/stranger at the door is done using three modules: Face Recognition, Voice Recognition and Similarity Index. These three modules are taken together to provide a percentage likelihood that the individual is in the “known” or “unknown” category. The system can also continuously monitor the health parameters of a vulnerable person living alone at home and aid them in calling for help in an emergency.

The authors have analyzed a number of existing biometric techniques to provide security for an individual living alone at home. These biometric techniques have been tested using MATLAB® image processing and signal processing toolboxes, and results have been calculated on the basis of recognition rate. A major contribution in providing security is a hybrid algorithm proposed by the author named PICA, which combines features of both PCA (Principle Component Analysis) and ICA (Independent Component Analysis) algorithms. This hybrid approach gives better performance recognition than either system alone. The second proposed hybrid algorithm for voice recognition is named as a MFRASTA algorithm by combining features of MFCC (Mel Frequency Cepstral Coefficient) and RASTA-PLP (RelAtive SpecTrA-Perceptual Linear Prediction) algorithm. After performing experiments, results are collected on the basis of recognition rate.

The authors have also proposed a third technique named as a Similarity Index to provide trust-based security for an individual. This technique is text independent in which a person is recognized by pronunciation, frequency, tone, pitch, etc., irrespective of the content spoken by the person. By combining these three techniques, a high recognition rate is provided to the person at the door and high security to the individual living independently at home. In the final contribution, the authors have proposed a fingertip-based application for health monitoring by using the concept of sensors. This application is developed using iPhone 6’s camera. When a person puts their fingertip on a camera lens, with the help of brightness of the skin, the person’s heartbeat will be monitored. This is possible even with a low-quality camera. In case of any emergency, text messages will be sent to the family members of the individual living alone by using 3G Dongle and MATLAB tool. Results show that the proposed work outperforms all the existing techniques used in face recognition, voice recognition, and health monitoring alone.

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Acknowledgments
List of abbreviations
Chapter 1: Overview of pervasive computing
	1.1 Overview of Pervasive Computing
	1.2 Distributed Computing
	1.3 Mobile Computing
	1.4 Pervasive Computing
		1.4.1 Pervasive computing examples
		1.4.2 Pervasive healthcare system
		1.4.3 Challenges of pervasive computing
		1.4.4 Issues in pervasive computing
		1.4.5 Advantages of pervasive computing
		1.4.6 Disadvantages of pervasive computing
	1.5 Biometrics
		1.5.1 Biometrics architecture
		1.5.2 Commonly used biometrics methods
			1.5.2.1 Fingerprint
			1.5.2.2 Face recognition system
				1.5.2.2.1 Challenges of face recognition
				1.5.2.2.2 Face recognition algorithms
			1.5.2.3 Hand geometry
			1.5.2.4 Iris scan
			1.5.2.5 Keystroke dynamics
			1.5.2.6 Dynamic signature verification
			1.5.2.7 Speech/voice recognition
				1.5.2.7.1 Voice recognition system
				1.5.2.7.2 Voice recognition techniques
			1.5.2.8 Facial thermograms
			1.5.2.9 Ear
			1.5.2.10 DNA
		1.5.3 Advantages
		1.5.4 Disadvantages
		1.5.5 Biometrics applications
	1.6 Sensor-Based Technology
	1.7 Individual Care in Pervasive Environment
		1.7.1 Neural networks
			1.7.1.1 Network architectures
	1.8 MATLAB 2012a
		1.8.1 Matrix concept
		1.8.2 Image processing in MATLAB
		1.8.3 Properties of image
		1.8.4 Signal processing in MATLAB
	1.9 Book Organization
Chapter 2: Hybrid face recognition PICA algorithm for individual's security at home
	2.1 Inroduction to Face Recognition
	2.2 Face Recognition Algorithms
		2.2.1 Principle component analysis (PCA)
		2.2.2 Independent component analysis (ICA)
		2.2.3 Linear discriminant analysis (LDA)
		2.2.4 Discrete cosine transform (DCT)
		2.2.5 Gabor wavelet
	2.3 Proposed Hybrid PICA Algorithm
	2.4 Implementation
	2.5 Results
	2.6 Summary
Chapter 3: Hybrid MFRASTA voice recognition technology for individual's security at home
	3.1 Introduction to Voice Recognition
	3.2 Challenges of Voice Recognition System
	3.3 Voice Recognition Algorithms
		3.3.1 Mel frequency cepstral coefficient (MFCC)
		3.3.2 Perceptual linear prediction (PLP)
		3.3.3 Linear prediction code (LPC)
		3.3.4 RelAtive SpecTrA-Perceptual Linear Prediction (RASTA-PLP)
		3.3.5 Zero crossing peak amplitudes (ZCPA)
		3.3.6 Dynamic time warping (DTW)
		3.3.7 Wavelet
	3.4 Proposed Hybrid MFRASTA Technique
	3.5 Similarity Index
	3.6 Implementation
	3.7 Results and Discussion
	3.8 Summary
Chapter 4: Sensor-based health monitoring system uses fingertip application
	4.1 Introduction to Sensors-Based Services
	4.2 Previous Techniques
		4.2.1 Phonocardiograph application
		4.2.2 Mercury sphygmomanometer
		4.2.3 Automatic digital sphygmomanometer
		4.2.4 Electrocardiographs
		4.2.5 Accelerometer-based application
	4.3 Proposed Technique: Fingertip Application
	4.4 Results
	4.5 Summary
Chapter 5: Hybrid PICA and MFRASTA technology with sensor-based fingertip application for individual's security at home
	5.1 Process of Face and Voice Recognition
	5.2 Vital Sign Monitoring
	5.3 Conclusion and Future Work
Chapter 6: Conclusion and future research
	6.1 Introduction
	6.2 Summary and Scope
	6.3 Future Work
References
Index