Open Access. Powered by Scholars. Published by Universities.®

Computer Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

Electrical and Computer Engineering

PDF

Old Dominion University

Face recognition

Publication Year

Articles 1 - 2 of 2

Full-Text Articles in Computer Engineering

A Subspace Projection Methodology For Nonlinear Manifold Based Face Recognition, Praveen Sankaran Jan 2009

A Subspace Projection Methodology For Nonlinear Manifold Based Face Recognition, Praveen Sankaran

Electrical & Computer Engineering Theses & Dissertations

A novel feature extraction method that utilizes nonlinear mapping from the original data space to the feature space is presented in this dissertation. Feature extraction methods aim to find compact representations of data that are easy to classify. Measurements with similar values are grouped to same category, while those with differing values are deemed to be of separate categories. For most practical systems, the meaningful features of a pattern class lie in a low dimensional nonlinear constraint region (manifold) within the high dimensional data space. A learning algorithm to model this nonlinear region and to project patterns to this feature …


Robust Face Representation And Recognition Under Low Resolution And Difficult Lighting Conditions, Mohammad Moinul Islam Apr 2002

Robust Face Representation And Recognition Under Low Resolution And Difficult Lighting Conditions, Mohammad Moinul Islam

Electrical & Computer Engineering Theses & Dissertations

This dissertation focuses on different aspects of face image analysis for accurate face recognition under low resolution and poor lighting conditions. A novel resolution enhancement technique is proposed for enhancing a low resolution face image into a high resolution image for better visualization and improved feature extraction, especially in a video surveillance environment. This method performs kernel regression and component feature learning in local neighborhood of the face images. It uses directional Fourier phase feature component to adaptively lean the regression kernel based on local covariance to estimate the high resolution image. For each patch in the neighborhood, four directional …