Facial Expression Recognition Using Subspace Learning On LBP

Abstract

There is different types of methods that can recognize the facial expression but none of them were able to generate the accurate result due to the lack of generalizability. This field has a huge possibilities and can open new doors to human machine interaction. As a result the demand of recognizing the human expression correctly is increasing day by day. So there are many ways to recognize the facial expression. Here in this paper, we are trying to analyze the facial expression on different sub space. First we applied a conventional method, LBP. Then we tried to apply Principal Component Analysis (PCA). We tried another subspace algorithm called Kernel Principal Component Analysis. Then we compared the results. We compared the accuracy of recognizing facial expression of these two algorithm using BSVM tool.