Michael Gray, Terrence J. Sejnowski, Javier Movellan
We examine eight different techniques for developing visual rep(cid:173) resentations in machine vision tasks. In particular we compare different versions of principal component and independent com(cid:173) ponent analysis in combination with stepwise regression methods for variable selection. We found that local methods, based on the statistics of image patches, consistently outperformed global meth(cid:173) ods based on the statistics of entire images. This result is consistent with previous work on emotion and facial expression recognition. In addition, the use of a stepwise regression technique for selecting variables and regions of interest substantially boosted performance.