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Computer Science > Computer Vision and Pattern Recognition

arXiv:0906.1763 (cs)
[Submitted on 9 Jun 2009 (v1), last revised 10 Jun 2009 (this version, v2)]

Title:Segmentation of Facial Expressions Using Semi-Definite Programming and Generalized Principal Component Analysis

Authors:Behnood Gholami (1), Allen R. Tannenbaum (2 and 3), Wassim M. Haddad (1) ((1) School of Aerospace Engineering, Georgia Institute of Technology (2) School of Electrical & Computer Engineering, Georgia Institute of Technology, (3) Department of Biomedical Engineering, Georgia Institute of Technology)
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Abstract: In this paper, we use semi-definite programming and generalized principal component analysis (GPCA) to distinguish between two or more different facial expressions. In the first step, semi-definite programming is used to reduce the dimension of the image data and "unfold" the manifold which the data points (corresponding to facial expressions) reside on. Next, GPCA is used to fit a series of subspaces to the data points and associate each data point with a subspace. Data points that belong to the same subspace are claimed to belong to the same facial expression category. An example is provided.
Comments: Corrected for typos and spacing errors
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:0906.1763 [cs.CV]
  (or arXiv:0906.1763v2 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.0906.1763

Submission history

From: Behnood Gholami [view email]
[v1] Tue, 9 Jun 2009 19:50:10 UTC (2,661 KB)
[v2] Wed, 10 Jun 2009 20:53:21 UTC (2,661 KB)
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