Please use this identifier to cite or link to this item:
Title: Feature regularization and extraction in eigenspace for face recognition
Authors: Bappaditya Mandal
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Biometrics
Issue Date: 2008
Source: Bappaditya, M. (2008). Feature regularization and extraction in eigenspace for face recognition. Doctoral thesis, Nanyang Technological University, Singapore.
Abstract: The ability to recognize human faces is a demonstration of incredible human intelligence. Over the last two decades researchers from diverse areas are making attempts to replicate this outstanding visual perception of human beings in machine recognition of faces. Within the face recognition literature, debate has been centered on how human beings perceive human faces and this has become an important and active research area. Psychologists concluded that holistic and component based approaches are dual routes to the face recognition. Recent studies (like FERET competition) show that holistic approaches have dominated the face recognition systems and have shown better performance than omponent based approaches. Although these holistic/appearance based approaches have attained certain level of maturity their performances are far away from the abilities of human recognition of faces. Owing to the immense potentiality of the face recognition applications it is imperative to develop a face recognition system, which is robust, e±cient and able to achieve high recognition accuracy on large face image databases. In this thesis, we propose various algorithms which are based on statistical pattern recognition and computer vision for robust face recognition with high accuracy.
DOI: 10.32657/10356/13278
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

Files in This Item:
File Description SizeFormat 
BappadityaMandal2008.pdfMain report3.26 MBAdobe PDFThumbnail

Page view(s) 50

Updated on Aug 3, 2021

Download(s) 20

Updated on Aug 3, 2021

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.