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 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.