Open AccessFace Recognition using PCA and LDA Technique for Noisy Faces
Author(s) -
Meeta Dubey,
Prashant K. Jain
Publication year - 2013
Publication title -
international journal of electrical and electronics research
Language(s) - English
Resource type - Journals
ISSN - 2347-470X
DOI - 10.37391/ijeer.010201
Subject(s) - artificial intelligence , facial recognition system , pattern recognition (psychology) , principal component analysis , computer science , euclidean distance , face (sociological concept) , feature extraction , noise (video) , speech recognition , computer vision , image (mathematics) , social science , sociology
Face recognition is always a popular area of research. There are various techniques used in the face recognition system. Principal component analysis (PCA) and linear discriminate analysis (LDA) techniques are the two most well-known techniques for the face recognition. In this paper, the PCA and LDA technique based face recognition system are described. The performance of this technique is compare in term of PSNR and RMSE for noisy image. The Euclidean distance between feature templates and database futures are used for identifying the face image. There are basically three types of noises present, but in this paper I am going to compare the salt and pepper noise with the Gaussian noise in the detailed and analytical ways. After finding the features of the different noisy images I am going to compare both the PCA and LDA technique for the noisy pictures.