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

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

2009-06-09

arxiv.org/abs/0906.1763v2

Computer Science

Computer Vision and Pattern Recognition

Corrected for typos and spacing errors

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Segmentation of Facial Expressions Using Semi-Definite Programming and Generalized Principal Component Analysis does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Segmentation of Facial Expressions Using Semi-Definite Programming and Generalized Principal Component Analysis, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Segmentation of Facial Expressions Using Semi-Definite Programming and Generalized Principal Component Analysis will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-65346