Journals Proceedings

Abstract

Face recognition has recently attracted increasing attention and is beginning to be applied in a variety of domains, predominantly for security. In the past few decades’ people used to create some passwords, key words or some other security measures to prevent maltransactions. But now a days with the advanced technology it is better to know the persons physical identity by recognizing some physical features. Because of the problem with traditional password/PIN system, biometric based technologies gained advantage. Face recognition is one of the biometric based technologies which use human faces for authentification. We use wavelet transform for the decomposition of images. Principal Component Analysis is used for the actual recognition. We considered Eigen faces as principal components Face images are projected onto a feature space (“face space”) that best encodes the variation among known face images. This approach transforms face images into a small set of characteristic feature images, called “Eigen faces”, which are the principal components of the initial training set of face images. Recognition is performed by projecting a new image into the subspace spanned by the Eigen faces (“face space”) and then classifying the face by comparing its position in face space with the positions of known.