A Comparison of Image Processing Techniques for Visual Speech Recognition Applications
We examine eight different techniques for developing visual representations in machine vision tasks. In particular we compare different versions of principal component and independent component 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 methods 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.