Please use this identifier to cite or link to this item: http://hdl.handle.net/1783.1/6075

3D reconstruction and synthesis of facial expressions using a manifold alignment framework

Authors Yu, Lap Fai
Issue Date 2009
Summary The capture, reconstruction and synthesis of facial expressions often involves specialized hardware support and considerable computation time. This prohibits its widespread deployment and use in real-time applications. In this paper, we aim at tackling this limitation via a learning-based approach, which is efficient and requires only modest hardware support. Our approach is based on a semi-supervised manifold alignment framework, where feature points extracted from 2D face images are aligned with data expressed as morph-target values for a 3D face model. By applying a kernel embedding method known as kernel locality preserving projections (KLPP) and a method for solving the pre-image problem in kernel methods, our framework is capable of handling nonlinearity and is defined everywhere. Experiments are conducted to demonstrate two possible applications of our proposed framework: 3D reconstruction of facial expressions and dynamic synthesis of facial expression sequences.
Note Thesis (M.Phil.)--Hong Kong University of Science and Technology, 2009
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Language English
Format Thesis
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