Photoplethysmogram Based Biometric Identification Incorporating Age, Gender and Time Variability

PHOTOPLETHYSMOGRAM BASED BIOMETRIC IDENTIFICATION INCORPORATING AGE, GENDER AND TIME VARIABILITY

SITI NURFARAH AIN BINTI MOHD AZAM

A dissertation submitted in fulfillment of the requirement for the degree of Master of Science (Communication Engineering)

Kulliyyah of Engineering International Islamic University Malaysia

MAY 2017

ABSTRACT

Biometric is the authentication and identification of a person by measuring or estimating their physiological characteristics. First generation biometric such as fingerprint, signature and voice have drawback and easily can be duplicated which lead to serious identity theft crime. Therefore, second generation of biometric was introduced by using bio-signal. This study evaluates the possibility of applying PPG as biometric identification system incorporating different age, gender group, and time variability. A total of 36 subjects were involved in this study consists of 18 males and 18 females for age difference and gender analysis. The PPG signals were taken in resting state by using pulse oximeter. The PPG signal was differentiated twice in order to form APG signal. These signals then undergo pre-processing and the segmentation process was done by using MATLAB. The highest peaks from the signal was used as reference point to determine the appropriate distance for one cycle of both signal. Then, the signals were classified by four commonly used classifiers which are Bayes Network, Naïve Bayes, Multilayer Perceptron, and Radial Basis Function. The outcome from this study suggested the accuracy up to 100% for different age group, 91.11% for female subjects and 95% for male subjects. For time variability analysis, a total of 5 PPG signals were collected from a publicly available online repository, which is MIMIC II Waveform Database, version 3, part 3 for two different periods and then undergoes pre-processing using a low pass filter. After that, the signals were segmented and later differentiated to produce APG signals and lastly the signals were classified using the classifiers mentioned. Based on the experimentation results, the accuracy obtained for PPG was up to 90% and for APG as high as 92.86%. To conclude, PPG and APG signals are capable to be used for biometric identification purposes as the results prove that even though the data were taken from different age, gender group and various time, the system is able to identify the person.

ii ملخص البحث

البيومرتية أو كام تسمى "تقنية ا إلحصاء احليوي" يه توثيق وحتديد ا ألشخاص من خالل قياس و تقدير خصائصهم الفيس يولوجية. يعاين ا إل نتاج ا ألول من البيومرتية املمتثةل يف بصامت ا ألصابع والتوقيع خبط اليد و بصمة الصوت من عيب كبري و هو أنه يسهل تكراره و هذا قد يؤدي إاىل جرامئ خطرية عن طريق رسقة الهوية. لهذا السبب ودلت احلاجة ل إالنتاج الثاين من البايومرتية املمتثل يف ا إلشارات احليوية. هذه ادلراسة تقوم بتقيمي إاماكنية تطبيق الفوتوبليثايسموغراف وهو قياس جحم عضو من أعضاء اجلسم عن طريق مكية ادلم املتواجدة يف هذا العضو اجلسدي كنوع من أنواع التعريف البايومرتي بس تخدام ا ألعامر اخملتلفه، و اجلنس، و التباين الوقيت .مض هذا البحث 63 خشص كعينة لدلراسة، 81 مهنم من اذلكور و 81 من ا إلانث .مت حفص فارق السن وحتليل نوع اجلنس، ومت حتصيل إاشارات الفوتوبليثايسم وغراف من عينات ادلراسة يف وضعية ا إلسرتخاء من خالل هجاز قياس ت أكسج النبض .مت متيزي إاشارة الفوتوبليثايسموغراف مرتني حىت يمت تكوين إاشارة تسارع خمطط التحجم. هذة ا إلشارات فامي بعد خضعت إاىل معليات املعاجلة املبدئية و التجزئة بس تخدام خمتربات املصفوفات .مت اس تخدام أعىل ا ألرقام املس تنتجة من ا إلشارة مكراجع لتحديد املسافة املالمئة لدلورة الواحدة ل إالشاراتن، كام مت تصنيف ا إلشارات من خالل ا ألربع مصنفات املشاع إاس تخداهما و يه: الش بكة البايزية، و الش بكة البايزية ا ألولية و املس تقبالت متعددة الطبقات، و وظيفة الشعاع ا ألس يايس. أ فادت نتاجئ هذه ادلراسة إاىل أن دقة التعارف تصل إاىل 811٪ للفئات العمرية اخملتلفة اب إلضافة دلقة تعارف تصل إاىل 18.88٪ لعينة ا إلانث و ٪19 لعينة اذلكور. أما ابلنس بة لتحليل التباين الوقيت، 9 من إاشارات الفوتوبليثايسموغراف مت جتميعها من مس تودع عام عىل ا ألن نرتت و هو قاعدة البياانت املوجية للرصد اذليك املتعدد املعامل يف العناية املركزية- رمق 2 )املعروف إابمس MIMIC II Waveform Database( النسخة الثالثة، اجلزء الثالث. و قد أس تخدم يف وقتني خمتلفني مث خضع ملعاجلة مبدئية إابس تخدام فلرت مترير منخفض. بعد ذكل مت تقس مي ا إلشارات مث مت يزيها إلنتاج إاشارات لتسارع خمطط التحجم و أخريًا مت تصنيف ا إلشارات حسب املصنفات اليت س بق ذكرها. و بناء عىل نتاجئ التجريب مت حتديد دقة إاشارات الفوتوبليثايسموغراف ملعدل 11%، و دقة إاشارات تسارع خمطط التحجمي ملعدل 12.13%. لتلخيص ما مت ذكره أ نف ًا، فإان إاشارات الفوتوبليثايسموغراف و إاشارات تسارع خمطط التحجمي ميكن إاس تغالهلام يف أغراض حتديد الهوية البيومرتية كام ثبت يف نتاجئ البحث أنه عىل الرمغ من أن البياانت قد مت حتصيلها من خمتلف ا ألعامر، و من مجموعات جنس ية خمتلفة و أوقات متباينة، فالنظام اكن قادرًا عىل حتديد هوية العينه اخملتربة.

iii

INTERNATIONAL ISLAMIC UNIVERSITY MALAYSIA

DECLARATION OF COPYRIGHT AND AFFIRMATION OF FAIR USE OF UNPUBLISHED RESEARCH

PHOTOPLETHYSMOGRAM BASED BIOMETRIC IDENTIFICATION INCORPORATING AGE, GENDER AND TIME VARIABILITY

I declare that the copyright holder of this thesis/dissertation is International Islamic University Malaysia.

No part of this unpublished research may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording or otherwise without prior written permission of the copyright holder except as provided below.

1. Any material contained in or derived from this unpublished research may be used by others in their writing with due acknowledgement.

2. IIUM or its library will have the right to make and transmit copies (print or electronic) for institutional and academic purposes.

3. The IIUM library will have the right to make, store in a retrieval system and supply copies of this unpublished research if requested by other universities and research libraries.

By signing this form, I acknowledged that I have read and understand the IIUM Intellectual Property Right and Commercialization policy.

Affirmed by Siti Nurfarah Ain binti Mohd Azam

………………………….. …………………… Signature Date

iv APPROVAL PAGE

I certify that I have supervised and read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Master of Science (Communication Engineering).

...... Khairul Azami Sidek Supervisor

...... Rashidah Funke Olanrewaju Co-supervisor

I certify that I have read this study and that in my opinion it conforms to acceptable standards of scholarly presentation and is fully adequate, in scope and quality, as a dissertation for the degree of Master of Science (Communication Engineering).

...... Hasmah Mansor Internal Examiner 1

...... Huda Adibah Mohd Ramli Internal Examiner 2

This dissertation was submitted to the Department of Electrical and Computer Engineering and is accepted as a fulfilment of the requirement for the degree of Master of Science (Communication Engineering)...... Anis Norashikin Nordin Head, Department of Electrical and Computer Engineering

This dissertation was submitted to the Kulliyyah of Engineering and is accepted as a fulfilment of the requirement for the degree of Master of Science (Communication Engineering)...... Erry Yulian Triblas Adesta Dean, Kulliyyah of Engineering

v DECLARATION

I hereby declare that this dissertation is the result of my own investigations, except where otherwise stated. I also declare that it has not been previously or concurrently submitted as a whole for any other degrees at IIUM or other institutions.

Siti Nurfarah Ain binti Mohd Azam

Signature……………………………. Date……………………..

vi ACKNOWLEDGEMENTS

In the name of Allah the Most Gracious and Merciful, Alhamdulillah that I managed to complete this Master Dissertation.

First and foremost, I would like to give my highest gratitude to my supervisor, Dr Khairul Azami Sidek for all the suggestions, recommendations, critics, and guidance along this semester. Without his support and encouragement, this dissertation might be incomplete. Therefore, thank you and only Allah can repay all your good deeds.

Not to forget, special thanks to my parents and siblings for the support and love that I’ve received since day one. Thank you so much. To all my friends who helped me and guided me through this project, thank you.

“Truly, Allah will admit those who believe (in the Oneness of Allah Islamic Monotheism) and do righteous good deeds, to Gardens underneath which rivers flow (in Paradise), wherein they will be adorned with bracelets of gold and pearls and their garments therein will be of silk.” - Quran 22: 23

vii TABLE OF CONTENTS

Abstract ...... ii Abstract in Arabic ...... iii Copyright Page ...... vi Approval Page ...... v Declaration ...... vii Acknowledgements ...... vi List of Tables ...... xi List of Figures ...... xii List of Abbreviation ...... xvi List of Symbols ...... xvii

CHAPTER ONE: INTRODUCTION ...... 1 1.1 Introduction ...... 1 1.2 Problem Statement ...... 4 1.3 Research Objectives ...... 5 1.4 Significance of Research ...... 5 1.5 Limitation of Study ...... 6 1.6 Dissertation Organization ...... 7

CHAPTER TWO: LITERATURE REVIEW ...... 8 2.1 Introduction ...... 8 2.2 Background Study ...... 8 2.2.1 Definition of Biometric ...... 8 2.2.2 Characteristic of Biometric ...... 9 2.2.3 Modes of Biometric Recognition ...... 9 2.2.4 Types of Biometric Modality ...... 11 2.3 Types of Biomedical Signal ...... 14 2.3.1 Electroencephalogram (EEG) ...... 15 2.3.1.1 Definition ...... 15 2.3.1.2 Characteristic of EEG signal ...... 15 2.3.2 Electrocardiogram (ECG) ...... 16 2.3.2.1 Definition ...... 16 2.3.2.2 Characteristic of ECG signal ...... 17 2.3.3 Photoplethysmogram (PPG)...... 18 2.3.3.1 Definition ...... 18 2.3.3.2 Characteristic of PPG signal ...... 19 2.3.4 Acceleration Plethysmogram (APG) ...... 20 2.3.4.1 Definition ...... 20 2.3.4.2 Characteristic of APG signal ...... 20 2.4 Related Work of PPG as A Biometric Modality ...... 21 2.5 Related Work of APG as A Biometric Modality ...... 27 2.6 Summary ...... 31

viii CHAPTER THREE: METHODOLOGY ...... 32 3.1 Introduction ...... 32 3.2 Signal Acquisition ...... 33 3.2.1 Hardware Module ...... 33 3.2.1.1 Easy Pulse Sensor ...... 33 3.2.1.2 Arduino UNO ...... 35 3.2.2 Software Module ...... 36 3.2.2.1 Coolterm Software ...... 36 3.2.2.2 Arduino Software (IDE) ...... 37 3.2.3 Data Collection ...... 38 3.2.3.1 The Connection of Arduino and Easy Pulse Sensor ....38 3.2.3.2 Data Acquisition Process ...... 39 3.3 Pre-processing ...... 43 3.4 Feature Extraction ...... 46 3.4.1 Segmentation ...... 46 3.4.2 Signal Differentiation ...... 48 3.5 Classification ...... 49 3.5.1 Bayes Network (BN) ...... 50 3.5.2 Naïve Bayes (NB) ...... 50 3.5.3 Multilayer Perception (MLP) ...... 51 3.5.4 Radial Basis Function (RBF) ...... 53 3.6 Summary ...... 54

CHAPTER FOUR: PHOTOPLETHYSMOGRAM BASED BIOMETRIC IDENTIFICATION INCORPORATING DIFFERENT AGE AND GENDER GROUP ...... 55 4.1 Introduction ...... 55 4.2 System and Methodology ...... 55 4.2.1 Signal Acquisition ...... 55 4.2.2 Pre-processing ...... 56 4.2.3 Feature Extraction ...... 56 4.2.4 Classification ...... 56 4.3 Experimentation and Results...... 57 4.4 Summary ...... 73

CHAPTER FIVE: PHOTOPLETHYSMOGRAM BASED BIOMETRIC IDENTIFICATION INCORPORATING TIME VARIABILITY ...... 74 5.1 Introduction ...... 74 5.2 System and Methodology ...... 74 5.2.1 Signal Acquisition ...... 74 5.2.2 Pre-processing ...... 75 5.2.3 Feature Extraction ...... 75 5.2.4 Classification ...... 76 5.3 Experimentation and Results...... 76 5.4 Summary ...... 83

CHAPTER SIX: CONCLUSION AND FUTURE WORKS ...... 85 6.1 Conclusion...... 85 6.2 Future Works ...... 87

ix REFERENCES ...... 89

LIST OF PUBLICATIONS ...... 93

APPENDIX ...... 95

x LIST OF TABLES

Table 2.1 Table of Comparison of Related Works on PPG signal 26

Table 2.2 Table of Comparison of Related Works on APG signal 30

Table 4.1 Group division for every age range 56

Table 5.1 Classification Results 82

Table 5.2 The Increment Percentage of Accuracy 83

xi LIST OF FIGURES

Figure 1.1 Identity Theft and Fraud Complaints 2

Figure 2.1 Biometric matching operation between enrolment and 9 recognition dataset

Figure 2.2 Block diagrams of enrollment, verification, and 10 identification modes

Figure 2.3 Types of biometric modalities: (a) DNA, (b) ear, (c) face, (d) facial, (e) hand and hand vein infrared thermogram, (f) gait, (g) hand and finger geometry, (h) iris, (i) 12 keystroke, (j) odor, (k) palm print, (l) retinal scan, (m) signature and (n) voice

Figure 2.4 The EEG signal 15

Figure 2.5 The frequencies of EEG signal 17

Figure 2.6 The formation of ECG waveform 17

Figure 2.7 Fingertip is attached with pulse oximeter to monitor the 18 blood pressure

Figure 2.8 The acquired PPG signal 18

Figure 2.9 Systolic and diastolic peaks of PPG signals 19

Figure 2.10 The APG signal waveform 20

Figure 3.1 The proposed identification system 32

Figure 3.2 The Easy Pulse Version 1.1 33

Figure 3.3 HRM-2511E as a transmission PPG probe 34

Figure 3.4 Arduino Uno 35

Figure 3.5 CoolTerm Window 36

Figure 3.6 The Arduino Uno Software 37

Figure 3.7 The connection between Easy Pulse and Arduino Uno 38

Figure 3.8 The Arduino program code to capture PPG data 39

Figure 3.9 Baud Rate Setting Window 40

xii Figure 3.10 The experiment set up 41

Figure 3.11 CoolTerm Window 42

Figure 3.12 Data acquisition setting 42

Figure 3.13 The recorded data in .txt file 42

Figure 3.14 HRM-25111E pulse sensor circuit 43

Figure 3.15 Stage 1 of filtering and amplification 44

Figure 3.16 Stage II Instrumentation Circuit 45

Figure 3.17 Digital Pulse Output Circuit 47

Figure 3.18 One cycle of PPG signal 47

Figure 3.19 Segmentation of PPG signal 47

Figure 3.20 The APG signal waveform 48

Figure 3.21 The illustration of (b) APG signal from (a) PPG signal 49

Figure 3.22 Naïve Bayes basic architecture 50

Figure 3.23 A Multilayer Perceptron Network 52

Figure 3.24 General architecture of RBF Network 53

Figure 4.1 (a) Raw PPG signal for Subject 14, (b) Filtered PPG 57 signal for Subject 14, and (c) APG signal for Subject 14

Figure 4.2 (a) Raw PPG signal for Subject 25, (b) Filtered PPG 58 signal for Subject 25, and (c) APG signal for Subject 25

Figure 4.3 (a) Raw PPG signal for Subject 19, (b) Filtered PPG 58 signal for Subject 19, and (c) APG signal for Subject 19

Figure 4.4 (a) Raw PPG signal for Subject 36, (b) Filtered PPG 59 signal for Subject 36, and (c) APG signal for Subject 36

Figure 4.5 Reference point of PPG signal for Subject 14 60

Figure 4.6 Reference point of PPG signal for Subject 25 60

Figure 4.7 Reference point of PPG signal for Subject 19 61

Figure 4.8 Reference point of PPG signal for Subject 36 61

Figure 4.9 Reference point of APG signal for Subject 14 62

xiii Figure 4.10 Reference point of APG signal for Subject 25 62

Figure 4.11 Reference point of APG signal for Subject 19 63

Figure 4.12 Reference point of APG signal for Subject 36 63

Figure 4.13 Segmentation of PPG signal for Subject 14 64

Figure 4.14 Segmentation of PPG signal for Subject 25 64

Figure 4.15 Segmentation of PPG signal for Subject 19 65

Figure 4.16 Segmentation of PPG signal for Subject 36 65

Figure 4.17 Segmentation of APG signal for Subject 14 66

Figure 4.18 Segmentation of APG signal for Subject 25 66

Figure 4.19 Segmentation of APG signal for Subject 19 67

Figure 4.20 Segmentation of APG signal for Subject 36 67

Figure 4.21 Classification accuracy of PPG signal based on age group 68

Figure 4.22 Classification accuracy of APG signal based on age group 69

Figure 4.23 Classification accuracy of PPG signal based on gender 71

Figure 4.24 Classification accuracy of APG signal based on gender 72

Figure 5.1 Segmentation of PPG Signal 75

Figure 5.2 Raw PPG signals for two different days for Subject 1 76

Figure 5.3 Raw PPG signals for two different days for Subject 2 77

Figure 5.4 Raw PPG signals for two different days for Subject 3 77

Figure 5.5 (a) Raw PPG signal for Subject 1 Day 1, (b) Filtered PPG signal for Subject 1 Day 1, and (c) APG signal for Subject 78 1 Day 1

Figure 5.6 (a) Raw PPG signal for Subject 1 Day 2, (b) Filtered PPG signal for Subject 1 Day 2, and (c) APG signal for Subject 78 1 Day 2

Figure 5.7 (a) Raw PPG signal for Subject 2 Day 1, (b) Filtered PPG signal for Subject 2 Day 1, and (c) APG signal for Subject 79 2 Day 1

xiv Figure 5.8 (a) Raw PPG signal for Subject 2 Day 2, (b) Filtered PPG signal for Subject 2 Day 2, and (c) APG signal for Subject 79 2 Day 2

Figure 5.9 (a) Raw PPG signal for Subject 3 Day 1, (b) Filtered PPG signal for Subject 3 Day 1, and (c) APG signal for Subject 80 3 Day 1

Figure 5.10 (a) Raw PPG signal for Subject 3 Day 2, (b) Filtered PPG signal for Subject 3 Day 2, and (c) APG signal for Subject 80 3 Day 2

Figure 5.11 Segmented PPG and APG signal of Subject 1 81

Figure 5.12 Segmented PPG and APG signal of Subject 2 81

Figure 5.13 Segmented PPG and APG signal of Subject 3 82

xv LIST OF ABBREVIATIONS

APG Acceleration Photoplethysmogram BN Bayes Network ECG Electrocardiogram HRV Heart Rate Variability IPI Interpulse Interval ISO International Organization for Standardization MATLAB Matrix Laboratory MIMIC Massachusetts Institute of Technology-Beth Israel Hospital MLP Multilayer Perceptron NB Naïve Bayes PIN Personal Identification Number PPG Photoplethysmogram RBF Radial Basis Function WEKA Waikato Environment for Knowledge Analysis

xvi LIST OF SYMBOLS

y(m) Differentiated PPG signals n Total number of sample cases x PPG amplitudes

N Level of decomposition x(m) PPG signal ci Center vector of neuron x Real number

휙 Radial Basis Function

휔푖 Weight of neurons

xvii CHAPTER ONE

INTRODUCTION

1.1 INTRODUCTION

Since 1992, identity theft and fraud cases had been recorded and subsequently a lot of precautions has been taken in order to protect an individual’s personal identity. Identity theft is a crime in which an imposter gains key pieces of personal information, for example, social security number or driver's license numbers where the purpose is to impersonate another person. The data can be used to attain credit card information, goods, and services in the name of the victim and provide the deceiver with fake credentials and information. In addition to running up debt, an imposter might provide false identification to the authority, thus creating a criminal record or even worse leaving outstanding arrest warrants for the person whose identity has been taken without him knowing it.

Javelin Strategy & Research found that based on 2015 Identity Fraud Study,

$16 billion were stolen from 12.7 million U.S. consumers in 2014, as compared to $18 billion and 13.1 million victims in 2013 (Pascual, 2015). It is recorded that there was a new identity fraud victim every two seconds in 2014 which indicates that this issue is getting serious and crucial. According to a survey conducted (Mannino, 2014), 66% of consumers who have had fraudulent charges were the first to notice them, not their financial institution. According to the FTC’s Consumer Sentinel Network Data Book

(Federal Trade Commission, 2014), the maximum percentage of identity theft targets were between age of 20-29 usually involving college students.

Figure 1. 1 Identity Theft and Fraud Complaints (Mannino, 2014)

Figure 1 illustrates identity theft and fraud complaints for over 10 million consumer fraud that have been filed with federal, state and local law enforcement agencies and private organizations from 2012 to 2014 based on Consumer Sentinel database. In 2014, it is recorded that over 2.5 million complaints were filed which indicates the seriousness of this issue.

Identity theft is a type of crime that is increasingly affecting more and more people. The worst is that, the person is not knowing their identity has been misused.

The London Metropolitan police recently calculated that identity fraud cost almost £14 billion in 2008. This works out as an average cost of £230 for every person in the UK.

This get worsen when identity fraud also involved children as victims. A total of 10.2% of the children had someone else using their social security number which is 51 times higher than adults (Power, 2011). We can assume that fraudsters are getting shrewder and ingenious to steal personal identity. Therefore, we need a more intelligent and dependable security system.

2 It is very crucial and important today that every person’s identity and privacy is being protected. The reliability of traditional security system have drawbacks and limitations. Therefore, biometric recognition system was introduced. Traditional authentication system were based on user’s knowledge, such as passwords or personal identification number (PIN), that can be forgotten, smartcards and cardkey can be lost and misplaced which indicates the weakness of the traditional systems.

On the other hand, biometric can provide solution for better security system and has been recognize as one of the most reliable technologies for future human identification and verification (Spachos, 2011). It is believed that biometric can solve many of the security issues and have better potential in replacing the traditional security methods.

Biometrics is the authentication and identification of a person by measuring or estimating their physiological characteristics (Zhang, 2009). According to International

Organization for Standardization (ISO), biometric is defined as “the automated recognition of individual based on their behavorial and biological characteristics”

(International Organization for Standard, 1997). Biometric is categorized into two; static and dynamic. Fingerprints, eye retina and iris are the examples of static biometric.

Static biometric are designed to supervise and restrict access to authentication system.

They are relatively universal, distinct, permanent and easy to collect. However, there are few drawbacks of static biometric and needs to be improved. There is a slight chance that, for example, fingerprints can be duplicated. In order to rule out the probability of any fraudster duplicating replica of the finger, there is a need of crosschecking the identity of the person by scanning a live finger. Therefore, dynamic biometric identification approaches are introduced.

3 Heart rate variability (HRV), interpulse interval (IPI), electrocardiogram (ECG) and photoplethysmogram (PPG) are the examples of dynamic biometrics. Dynamic biometrics traits are better when compared with conventional biometric, as their characteristics are random and time-variance (Zhang, 2009). Numerous biometric measures have been studied for identification purposes, however, these method are not the same level of system complexity, cost and accuracy.

Thus, this study will propose the use of PPG signals as biometric identification system. PPG signals are low in deployment cost, easy to use, smaller in size and conveniently can be used to various parts of human body such as finger, ear lobe, wrist or arm (Spachos, 2011). PPG is a pulsatile signal that synchronizes with the heartbeat and possesses a waveform close to the arterial blood pressure waveform obtained through direct catheterization (Meredith, 2012).

1.2 PROBLEM STATEMENT

Photoplethysmogram (PPG) was previously implemented to measure the oxygen saturation, blood pressure, cardiac output, and for evaluating autonomic functions. It is a promising technology due to its simplicity, low cost and non-invasiveness. In recent years, PPG signals have been used for biometric recognition. However, to the best of our knowledge, there have been little research on the implementation of PPG signals as a biometric recognition system incorporating different age, gender group, and time variability. According to a study, heart rate of an individual varies with age due to the changes of the cardiovascular system against aging (Voss, 2013). The study discovered that, age is the primary factor that affect the heart rate analysis while gender only have a slight significance change but only among age group of 55 to 74 for female subjects.

4 The feasibility of applying PPG signals as a biological discriminant has been preliminary studied. However, the underlying issues that governs a practical biometric system have not been properly addressed. The objective of this work is to increase user acceptability of PPG based biometric identification by incorporating gender and age variability. Previous works have been more focused on person identification using normal subjects without considering conditions which could promote fluctuation of

PPG signals. Thus, in this study, we will investigate the possibility of categorizing individual built upon their PPG morphological signal. This technique will improve the current identification system by providing a complement which will be able to reduce cases involving identity crime.

1.3 RESEARCH OBJECTIVES

This research consists of three objectives:

1) To study personal identification mechanism using photoplethysmography

based biometric identification system incorporating age variability,

gender and different time instances.

2) To develop an innovative biometric sample extraction technique that is

simple and accurate using PPG signals with different age groups, gender

and time variability.

3) To evaluate the performance biometric identification on PPG signals

incorporated with different age group, gender and time variability.

1.4 SIGNIFICANCE OF THE RESEARCH

PPG signals provide a non-invasive, easy-to-use and accurate method to obtain valuable physiological information. The feasibility of applying PPG signals as a

5 biological discriminant has been preliminary studied. The potential of applying PPG signals for biometric recognition is promising due to its superior characteristics.

Thus, in this study, we will develop a robust and reliable PPG based biometric identification system incorporating this factor. Simple yet effective biometric sample extraction techniques will be proposed to select unique and discriminant attributes. The expected results would enable person identification regardless of age variability, gender group, and at different time instances. The output is significant since PPG signal is a low cost, easy-to-use and low powered device which would be practical in various security applications such as airport terminals, customs borders, financial institutions and other applications which requires protection of private and confidential data.

1.5 LIMITATION OF THE STUDY

There are a few limitations of this study that need to be taken into account. While taking and collecting data of PPG signal, there are several requirements that must be fulfilled.

The reading of the signal must be measured in a controlled environment where the subject must be healthy and in resting condition. The main objective of this study is to evaluate the ability of PPG signal to identify individuals while neglecting other relevant variability such as physiological conditions where a person is running or in stress and also pathological conditions for example having cold or suffering with heart abnormalities in order not to confuse and disturb the classification process. Other than that, an individual who is not in resting state or have certain heart condition contributes to different effect on biometric that might interrupt the system performance.

6 1.6 DISSERTATION ORGANIZATION

The remaining chapters in this dissertation are structured as follows:

Chapter Two provides a general idea of the related literatures available on PPG and

APG biometric. It also comprises other additional fundamental information to

assist the readers to fully grasp this work.

Chapter Three deliberates the existing PPG biometric and an algorithm based on them

is proposed to recognize individuals under different age groups, gender and time

variability. The system is designed and experimentally verified, with the

performance evaluated against a related study.

Chapter Four elaborates the analysis of the study based on different age and gender

group. The chapter proposed a technique and suitable classification system to

be applied for biometric identification method.

Chapter Five discusses on time variability analysis of PPG for person identification.

Chapter Six concludes the dissertation by summarizing the dissertation contributions,

and provides recommendation for future directions in which this field of

research can be furthered so that a sustainable system appropriate for various

situations can be developed.