A new image distance for KFDA
Zheng Cai, Fulong Wang, Ai-Hui Xu · 2010 3rd International Congress on Image and Signal Processing · 2010
We present a new image distance which we call IMage Matching Distance(IMMD). This distance considers the relationship between the every point of image and the specific area of corresponding image, finds matching point in this special area, to let the image of the gray level and its location introduced into the similarity measure of image. It makes IMMD have a good robustness for the changes of face posture, angle, and the expression. Embedding IMMD in kernel Fisher discriminant analysis(KFDA) for face recognition. The experimental results show that this method is superior than the same type method which embedded Traditional Euclidean Distance and Image Euclidean Distance.