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Title: Exploring face space: A computational approach
Keywords: face space, statistical learning, manifold learning
Issue Date: 31-Jul-2007
Citation: ZHANG SHENG (2007-07-31). Exploring face space: A computational approach. ScholarBank@NUS Repository.
Abstract: Face recognition has received great attention especially during the past few years. However, even after more than $30$ years of active research, face recognition, no matter using still images or video, is a difficult problem. The main difficulty is that the appearance of a face changes dramatically when variations in illumination, pose and expression are present. And attempts to find features invariant to these variations have largely failed. Therefore we try to understand how face image and identity are affected by these variations, i.e., pose and illumination. In this thesis, by using image rendering, we present a new approach to study the face space, which is defined as the set of all images of faces under different viewing conditions. Based on the approach, we further explore some properties of the face space. We also propose a new approach to learn the structure of the face space that combines the global and local information. Along the way, we explain some phenomena, which have not been clarified yet. We hope the work in this thesis can help to understand the face space better, and provide useful insights for robust face recognition.
Appears in Collections:Ph.D Theses (Open)

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