Segmentation of Facial Expressions Using Semi-Definite Programming and Generalized Principal Component Analysis
Behnood Gholami (1), Allen R. Tannenbaum (2, 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
TL;DR
This paper introduces a novel method combining semi-definite programming and generalized principal component analysis to effectively segment facial expressions by reducing data dimensionality and identifying subspace structures.
Contribution
The paper presents a new approach that integrates semi-definite programming with GPCA for facial expression segmentation, improving data analysis and classification accuracy.
Findings
Successfully distinguishes multiple facial expressions
Reduces data dimensionality effectively
Accurate subspace identification for expressions
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.
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Taxonomy
TopicsFace and Expression Recognition · Blind Source Separation Techniques