Fisher face recognition based on wavelet transform and cosine transform
Dai Hong-y · Information technology newsletter · 2014
This article combines several existing feature extraction algorithms for face recognition. Firstly,face images are decomposed by using wavelet transform,in which some noises have been removed from the images. Then,discrete cosine transform is used on low frequency components to get further feature extraction and compression,which is not sensitive to light,gesture or facial expression. After that,a combination of PCA and LDA is conducted to obtain final face features. Finally,Euclidean distance and the minimum distance classifier are used to perform face recognition. The simulation experiments based on ORL show a better recognition rate in this combination.