New image filtering technique combining a wavelet transform with a linear neural network: application to face recognition
Fan Yang · Optical Engineering · 2000
(In Press, 2000, Optical Engineering) In order to improve the performance of a linear auto-associator (which is a neural network model), we explore the use of several pre-processing techniques. The gist of our approach is to represent each pattern by one or several pre-processed (i.e. filtered) versions of the original pattern (plus the original pattern). First, we compare the performance of several pre-processing techniques (a plain vanilla version of the auto-associator as a control, a Sobel operator, a Canny-Deriche operator, and a multiscale Canny-Deriche operator) and a Wiener filter on a pattern completion task using a noise degraded version of faces stored. We found that the multiscale Canny-Deriche operator gives the best performance of all models. Second, we compare the performance of the multiscale Canny-Deriche operator with the control condition on a pattern completion task of noise degraded versions (with several levels of noise) of learned faces and new faces...