Facial Expression Recognition using kernel principal component analysis (KPCA)
Alvaro Angel Orozco, Mauricio A. Álvarez, Juan Gabriel Fetecua Valencia · 2008
This paper presents a methodology on the recognition of facial expressions with kernel principal component analysis using the Carnegie Mellon University database as a testing tool. This method uses a kernel function to map data from the original feature space to a higher dimensional space, through which a nonlinear problem is translated into a linear one and is to be solved in a linear way, besides a kernel based method can reduce the number of parameters used by the clasiffier, this method compares with principal component analysis and discussed where the percentages of sucess found with the database is greater than 90%.