|Title:||Low-resolution face recognition|
|Advisors:||Lam, Kin-man Kenneth (EIE)|
|Subject:||Hong Kong Polytechnic University -- Dissertations|
Human face recognition (Computer science)
|Department:||Department of Electronic and Information Engineering|
|Pages:||31, iv pages : color illustrations|
|Abstract:||Face recognition has got rapid development during this decade. However, low-resolution face recognition usually cannot achieve satisfactory performance, whether by using traditional methods or deep learning methods. In general, the amount of information and the usefulness of features will determine the face recognition rate. In this dissertation, after reviewing some of the previous works, we propose a practical multi-resolution method namely "MB-LBPD-MDS". Firstly, MTCNN is employed for the face detection stage. Secondly, for feature extraction, we have implemented "MB-LBPD" to make the Local Binary Pattern features become numerical so that it can be used to compute the covariance matrix for principal component analysis. Then, we use the MDS (multidimensional scaling) method to simultaneously project high-resolution (HR) images and low-resolution (LR) images into a unified latent subspace, where the distances of different classes approximate their distances in the HR space. The proposed approach is able to separate interclass faces accurately, because, the optimization iteration algorithm used can not only ensure the consistency for each LR face image and corresponding HR one, but also take both intraclass distances and interclass distances into consideration. Experiments on public face databases, like the ORL database and FERET database, show satisfactory results, and a large number of experiments have also been conducted in this project.|
|Rights:||All rights reserved|
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