FACE VERIFICATION WITH STATISTICAL MODELS OF SHAPE AND APPEARANCE

ABSTRACT

Research in computer vision and machine learning is a significant part of research in computer science departments of many leading institutions resulting in ideas and products that have direct applications in different industries such as medical image segmentation in the medical industry, and face recognition and tracking in the entertainment 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 University. 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.

CHAPTER 1

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. Our main goal for this thesis was to build a code repository for the computer vision and machine learning group focusing on research in face recognition and computer animation, and to utilize the code base to perform some experiments in face verification. The experiments performed evaluated the use of Mahalanobis distance, Euclidean distance, Manhattan distance and normalized correlation as metrics for face verification using parameters obtained from an appearance model. These measures were chosen because they are the most commonly used.

This chapter briefly describes the initial design of the code repository, the scope covered for this thesis, our contributions and the layout of the report.

Department: Computer Science (M.Sc Thesis)

Format: MS Word

Chapters: 1 - 5, Preliminary Pages, Abstract, References, Appendix.

No. of Pages: 57

Price: 20,000 NGN

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