Distorted face image segmentation in PCA with neural network

In this paper, we propose a new face image restoration method based on the features extracted from the noisy images given by the principal components of the noise covariance matrix. This technique deliberate the additive normal scattered degradation classic and use of a code shrinkage technique to remove noise from the images. The proposed work have been a large number of artificial neural networks and learning algorithms with PCA for distorted pixels to solve group face image feature extraction problems, most of them being adaptive in nature and well-suited for many real environments where adaptive approach is required as a well-known statistical technique for face feature extraction, data compression, multivariate data for serious of images.