ABSTRACT
in computer vision and learning is a significant part of research in computer departments of many leading institutions resulting in ideas and products that have direct applications in different industries such as medical image in the medical industry, and face recognition and tracking in the and security industry.
Face recognition is a significant part of research in computer vision and machine learning and has a wide range of applications in security, human computer interaction and artificial intelligence in general.
The main goal of this thesis was to build a code repository to facilitate research in computer vision and machine learning at The African University of Science and Technology, Abuja. Our work concentrated on implementing some statistical shape and appearance algorithms used in face recognition research.
We trained an appearance model and active shape models for an experiment in face verification. We evaluated the use of parameters from the appearance model for face verification using four very common metrics: Mahalanobis distance, Euclidean distance, normalized correlation and Manhattan distance.
Our results showed that normalized correlation performed least while there was very little difference in the performance of the others.
TABLE OF CONTENTS
Dedication . . . . . .. . . . i
Acknowledgements . . . . ii
Abstract . . . . . . . iii
List of Figures . .. . iv
List of Tables . . . . . . . vii
1 Introduction 1
1.1 Scope . . . . 2
1.2 Contribution . . . . . . 3
1.3 Layout . . . . . 3
2 Background and Literature Review 4
2.1 Statistical Models . . . . . . . . .. . . . 4
2.2 Active Shape Models . . .. . . . . 5
2.3 Active Appearance Models . . . . . . . . . 5
2.4 Differences between AAM and ASM . . . . . . . . . . . 6
2.5 Principal Component Analysis . . . . . 6
2.6 Linear Discriminant Analysis . . .. . . . 7
2.7 Distance and Similarity measures . . . . 8
2.7.1 Mahalanobis Distance . . . . . . . . . . . . . . 8
2.7.2 Euclidean Distance . . . . . 9
2.7.3 Manhattan Distance . .. . . . 9
2.7.4 Normalized Correlation . . . 9
2.8 Face Recognition 10
3 Active Shape Models 11
3.1 Landmarked Points . . . . . . 11
3.2 Aligning the Points . . . . . . . 12
3.3 Modeling Shape Variation . . . . 14
3.4 Modeling Local Structure . . . 15
3.5 Multi-resolution ASM . . . . . . . . 16
4 Appearance Model 19
4.1 Modeling the Texture variation . . . . 19
4.2 Combining the shape and texture model . . . . . . . . . 22
4.3 Fitting the Appearance Models Using Active Shape Model . . 23
5 Face Recognition 26
5.1 The Face Verification Framework . . . . . . . .. . . . 27
5.2 Evaluation of the Face Verification System . . . . . . . 27
5.3 Decision Threshold . . . . .. . . . . . 29
6 Experiments 31
6.1 Database . . . . . . .. . . . . . . . . 31
6.1.1 MUCT Dataset . . . . . . . . . . . . . . . . . . 31
6.1.2 Model Building .. . . . . . . . . . 32
6.1.3 Partitioning Data for Verification Experiment . . . . . 32
6.2 Face verification experiment . . . . . . . . . . . 33
6.3 Experiment: Quantitative comparison of the results of the use of four metric (Mahalanobis, Euclidean, Manhattan, Normalized Correlation). . . . . . 34
6.3.1 Learning the Global thresholds (Mahalanobis, Euclidean, Manhattan, Normalized Correlation . . . 34
6.3.2 Error Rate Comparison (Mahalanobis, Euclidean, Manhattan, Normalized Correlation) . . 37
6.4 Discussions . . . . . . . . . . 37
7 Conclusion 39
7.1 Summary of contributions . . . . . 39
7.2 Challenges . . . . . . . . . . . 39
7.3 Future works . . . . . . . . . . . . . 40
A Principal Component Analysis 41
B Principal Component Analysis for high dimensional data 41
References 43
INTRODUCTION
Research in computer vision and machine learning is a significant part of research in leading computer science departments worldwide. It has led to many breakthroughs in both academic research and commercial applications.
A currently thriving area of research in computer vision and machine learning is face recognition.
It was described in [30] as one of the most successful applications of image analysis and understanding, stating two reasons for the strong research efforts in this area as the wide range of applications it provides and the availability of the technology to support the research.
A study of some of the leading institutions in this field and in other fields in computer science reveals each has a thriving code repository which has been built over the years by researchers and is available to new researchers to build upon thus speeding up research work.
Examples include VisionX of Cornell University Vision and Image Analysis Group1. FSL, of the Analysis Group, FMRIB, Oxford, UK2 and STAIR Vision Library (SVL)3 developed by a Stanford PhD student for research initially to support the Stanford AI robot project.
One goal of the computer vision and machine learning (CVML) group at The Africa University of Science and Technology, Abuja is to build its own code repository from ground up to facilitate research within the group.
Many of the algorithms implemented for the repository are not freely available elsewhere or at least not in the organized form implemented in the repository.
REFERENCES
Cootes, T. F., Taylor, C. J., Cooper, D. H., & Graham, J. (1995). Active shape models- their training and application. Computer vision and image understanding, 61(1), 38-59.
Cootes, T. F., & Taylor, C. J. (2004). Statistical models of appearance for computer vision. Imaging Science and Biomedical Engineering, University of Manchester, Manchester M13 9
PT, UK March, 8.
Li, S. Z. (2011). Handbook of face recognition. Springer verlag London Limited.Viola, P., & Jones, M. J. (2004). Robust real-time face detection. International journal of computer vision, 57(2), 137-154.
Cootes, T. F., Edwards, G. J., & Taylor, C. J. (1998). Active appearance models. In Computer Vision—ECCV‟98 (pp. 484-498). Springer Berlin Heidelberg.
Cootes, T. F., & Taylor, C. J. (1992). Active shape models—„smart snakes‟. In BMVC92 (pp. 266-275). Springer London.
StudentsandScholarship Team
Be the first to comment