Face, Expression, and Iris Recognition Using Learning-Based Approaches

Abstract

This thesis investigates the problem of facial image analysis. Human faces contain a lot of information that is useful for many applications. For instance, the face and iris are important biometric features for security applications. Facial activity analysis such as face expression recognition is helpful for perceptual user interfaces. Developing new methods to improve recognition performance is a major concern in this thesis.

In approaching the recognition problem of facial image analysis, the key idea is to use learning-based methods whenever possible. For face recognition, we propose a face cyclograph representation to encode continuous views of faces, motivated by psychophysical studies on human object recognition. For face expression recognition, we apply a machine learning technique to solve the feature selection and classifier training problems simultaneously, even in the small sample case.

Iris recognition has high recognition accuracy among biometric features, however, there are still some issues to address to make more practical use of the iris. One major problem is how to capture iris images automatically without user interaction, i.e., not asking users to adjust their eye positions. Towards this goal, a two-camera system consisting of a face camera and an iris camera is designed and implemented based on facial landmark detection. Another problem is iris localization. A new type of feature based on texture difference is incorporated into an objective function in addition to image gradient. By minimizing the objective function, the iris localization performance can be improved significantly. Finally, a method is proposed for iris encoding using a set of specially designed filters. These filters can take advantage of efficient integral image computation methods so that the filtering process is fast no matter how big the filters are.