A Facial Expression Classification System Integrating Canny, Principal Component Analysis and Artificial Neural Network

Computer Science – Computer Vision and Pattern Recognition

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

6 pages, 10 figures, International Journal of Machine Learning and Computing, Vol. 1, No. 4, October 2011, ISSN (Online): 2010

Scientific paper

Facial Expression Classification is an interesting research problem in recent years. There are a lot of methods to solve this problem. In this research, we propose a novel approach using Canny, Principal Component Analysis (PCA) and Artificial Neural Network. Firstly, in preprocessing phase, we use Canny for local region detection of facial images. Then each of local region's features will be presented based on Principal Component Analysis (PCA). Finally, using Artificial Neural Network (ANN)applies for Facial Expression Classification. We apply our proposal method (Canny_PCA_ANN) for recognition of six basic facial expressions on JAFFE database consisting 213 images posed by 10 Japanese female models. The experimental result shows the feasibility of our proposal method.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

A Facial Expression Classification System Integrating Canny, Principal Component Analysis and Artificial Neural Network 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 A Facial Expression Classification System Integrating Canny, Principal Component Analysis and Artificial Neural Network, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and A Facial Expression Classification System Integrating Canny, Principal Component Analysis and Artificial Neural Network will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-349664

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.