Dynamic Descriptors in Human Gait Recognition by Tahir Amin A thesis submitted in conformity ...

128 Pages·2013·3.05 MB·English

Dynamic Descriptors in Human Gait Recognition
by
Tahir Amin
A thesis submitted in conformity with the requirements
for the degree of Doctor of Philosophy
Graduate Department of Electrical and Computer Engineering
University of Toronto
Copyright ⃝c 2013 by Tahir Amin
Abstract
Dynamic Descriptors in Human Gait Recognition
Tahir Amin
Doctor of Philosophy
Graduate Department of Electrical and Computer Engineering
University of Toronto
2013
Feature extraction is the most critical step in any human gait recognition system. Al-
though gait is a dynamic process yet the static body parameters also play an important
role in characterizing human gait. A few studies were performed in the past to assess the
comparative relevance of static and dynamic gait features. There is, however, a lack of
work in comparative performance analysis of dynamic gait features from different parts
of the silhouettes in an appearance based setup. This dissertation presents a comparative
study of dynamic features extracted from legs, arms and shoulders for gait recognition.
Our study partially supports the general notion of leg motion being the most important
determiningfactoringaitrecognition. Butitisalsoobservedthatfeaturesextractedfrom
upper arm and shoulder area become more significant in some databases. The usefulness
of the study hinges on the fact that lower parts of the leg are generally more noisy due to
a variety of variations such as walking surface, occlusion and shadows. Dynamic features
extractedfromtheupperpartofthesilhouettespossessignificantlyhigherdiscriminatory
power in such situations. In other situations these features can play a complementary
role in the gait recognition process.
We also propose two new feature extraction methods for gait recognition. The new
methods use silhouette area signals which are easy and simple to extract. A significant
performance increase is achieved by using the new features over the benchmark method
and recognition results compare well to the other current techniques. The simplicity and
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compactness of the proposed gait features is their major advantage because it entails low
computational overhead.
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Dedication
To my family
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Acknowledgements
First and foremost, I would like to extend my sincere thanks to my supervisor Prof. Dim-
itrios Hatzinakos for his encouragement, guidance, and continuous support throughout
my research work and writing of this manuscript. This work would have been impossible
without his feedback, patience and kindness. I consider it an honour to work with him.
I wish to express my gratitude to my thesis advisory committee members, Prof. Kon-
stantinos N. Plataniotis and Prof. Parham Aarabi, for providing me valuable feedback
and advice during my research work and writing of this manuscript. I would also like to
thank Prof. Glenn Gulak for his insightful input and comments on my thesis. My sincere
gratitude is due to Prof. Svetlana Yanushkevic for sparing her valuable time to serve as
external examiner for my final oral examination. My thanks are due to the Department
of Electrical and Computer Engineering, University of Toronto for providing research and
computing resources.
I would like to acknowledge my supervisor’s funding resources National Sciences and
Engineering Research Council of Canada (NSERC) for financial support to this research
work. IwouldalsoliketoacknowledgetheOntarioGraduateScholarship(OGS)program
for providing me financial support during the early years of my research work.
I am thankful to my colleagues and members of the Multimedia Laboratory for cre-
ating a friendly and congenial environment in the Lab. It was a pleasure to work with
Dr. Yongjin Wang, Dr. Francis Bui, Dr. Foteini Agrafioti and other graduate students.
Last but not least, I owe my deepest gratitude to my family, my parents, siblings,
beloved wife and son Daniel for their unconditional love, encouragement, understanding
and support. Their love has kept me going through some of the most difficult and
challenging times of my life.
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Contents
List of Tables x
List of Figures xii
List of Abbreviations xv
1 Introduction 1
1.1 Motivation and Applications . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Biometric Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Gait Biometric . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.5 Body Dynamics and Gait Recognition . . . . . . . . . . . . . . . . . . . . 16
1.6 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.7 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2 Literature Review 21
2.1 Model Based Gait Recognition . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2 Appearance Based Gait recognition . . . . . . . . . . . . . . . . . . . . . 29
2.3 Miscellaneous Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.4 Dynamic versus Static Features . . . . . . . . . . . . . . . . . . . . . . . 41
2.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
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3 Determinants in Gait Recognition 44
3.1 Extraction of Body Dynamics . . . . . . . . . . . . . . . . . . . . . . . . 44
3.2 Correlation Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
3.3 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.3.1 Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.3.2 Feature Vector Normalization . . . . . . . . . . . . . . . . . . . . 53
3.3.3 Feature Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.3.4 Comparative Performance of Dynamic Features . . . . . . . . . . 58
3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
4 Correlation based Gait Recognition 64
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.2 Silhouette Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.3 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
4.3.1 Correlation Features . . . . . . . . . . . . . . . . . . . . . . . . . 67
4.3.2 Fourier Descriptor Features . . . . . . . . . . . . . . . . . . . . . 69
4.4 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.4.1 Feature Database . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4.4.2 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 72
4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
5 Wavelet Analysis of Human Gait 79
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
5.2 Extraction of Wavelet Features . . . . . . . . . . . . . . . . . . . . . . . 81
5.3 Experimental Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5.3.1 Performance of Different Wavelet Kernels . . . . . . . . . . . . . . 83
5.3.2 Choosing Level of Decomposition . . . . . . . . . . . . . . . . . . 84
5.3.3 Identification Results . . . . . . . . . . . . . . . . . . . . . . . . . 85
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5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
6 Conclusion and Future Work 91
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
6.2 Future Research Extension . . . . . . . . . . . . . . . . . . . . . . . . . . 93
A Empirical Mode Decomposition (EMD) 95
B Implementation Details 98
B.1 Baseline Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
B.2 Mass Vector Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
C Wavelet Transform 100
C.1 Continuous Wavelet Transform . . . . . . . . . . . . . . . . . . . . . . . 100
C.2 Discrete Wavelet Transform . . . . . . . . . . . . . . . . . . . . . . . . . 102
Bibliography 104
viii
List of Tables
2.1 Model based gait recognition research . . . . . . . . . . . . . . . . . . . 30
2.2 Appearance based gait recognition techniques . . . . . . . . . . . . . . . 39
3.1 Summary of gait databases . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.2 GTech probe sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.3 Number of sequences for each possible combination . . . . . . . . . . . . 56
3.4 Probe set for each of the gait challenge experiments . . . . . . . . . . . . 57
3.5 Probe set for gait challenge experiments A–G . . . . . . . . . . . . . . . 58
3.6 Comparison of features at rank 1 and rank 5 for GC database . . . . . . 60
3.7 Comparison of features at rank 1 and rank 5 for GTech database . . . . . 62
4.1 Recognition results for probe 1 . . . . . . . . . . . . . . . . . . . . . . . . 73
4.2 Recognition results for probe 3 . . . . . . . . . . . . . . . . . . . . . . . 75
4.3 Recognition results for probe 4 . . . . . . . . . . . . . . . . . . . . . . . 75
4.4 Recognition results for probe 5 . . . . . . . . . . . . . . . . . . . . . . . 78
5.1 Comparison of db1, db2 and db4 based features at rank 1 . . . . . . . . . 84
5.2 Comparison of db1, db2 and db4 based features at rank 5 . . . . . . . . . 85
5.3 Performance comparison for probe 1 . . . . . . . . . . . . . . . . . . . . . 86
5.4 Performance comparison for probe 2 . . . . . . . . . . . . . . . . . . . . . 87
5.5 Performance comparison for probe 3 . . . . . . . . . . . . . . . . . . . . . 87
5.6 Performance comparison for probe 4 . . . . . . . . . . . . . . . . . . . . . 88
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5.7 Performance comparison for probe 5 . . . . . . . . . . . . . . . . . . . . . 88
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