Tag Archives: Linear Discriminant Analysis

A Novel Method Of Average Filtering For Removing Noise And Face Recognition (Published)

Face recognition is new and difficult which requires great effort and determination due to the Wide variety of faces, complexity of noises and image backgrounds. In this paper, we propose an Average Filtering based novel method for face recognition in cluttered and noisy images. It is imperative that computational researchers know of the key findings from experimental studies of face recognition by human. These findings provide insights into the nature of starting symbol to begin that the human visual system relies upon for achieving its great deal of performance and serve as the building blocks for efforts to artificially emulate these abilities. In this paper, we are presenting what we believe are various basic results, with implications for the computational design systems. The aim of our proposed work of average filtering based method for face recognition is to improve the recognition accuracy. We use AT&T face database and experiments on it are performed to demonstrate the effectiveness of the proposed method.

Keywords: Average Filter, Eigenfaces, Face Recognition, Feature Extraction, Fisherfaces., Laplacianfaces, Linear Discriminant Analysis, Principal Component Analysis, Smooth Mean Filter