Face recognition using different training data.

Li Zhifeng.Thesis submitted in: December 2002.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 49-53).Abstracts in English and Chinese.Abstract --- p.iAcknowledgments --- p.vTable of Contents --- p.viList of Figures --- p.viiiList of Tables --- p.ixChapter Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Face Recognition Problem and Challenge --- p.1Chapter 1.2 --- Applications --- p.2Chapter 1.3 --- Face Recognition Methods --- p.3Chapter 1.4 --- The Relationship Between the Face Recognition Performance and Different Training Data --- p.5Chapter 1.5 --- Thesis Overview --- p.6Chapter Chapter 2 --- PCA-based Recognition Method --- p.7Chapter 2.1 --- Review --- p.7Chapter 2.2 --- Formulation --- p.8Chapter 2.2.1 --- Karhunen-Loeve transform (KLT) --- p.8Chapter 2.2.2 --- Multilevel Dominant Eigenvector Estimation (MDEE) --- p.12Chapter 2.3 --- Analysis of The Effect of Training Data on PCA-based Method --- p.13Chapter Chapter 3 --- LDA-based Recognition Method --- p.17Chapter 3.1 --- Review --- p.17Chapter 3.2 --- Formulation --- p.18Chapter 3.2.1 --- The Pure LDA --- p.18Chapter 3.2.2 --- LDA-based method --- p.19Chapter 3.3 --- Analysis of The Effect of Training Data on LDA-based Method --- p.21Chapter Chapter 4 --- Experiments --- p.23Chapter 4.1 --- Face Database --- p.23Chapter 4.1.1 --- AR face database --- p.23Chapter 4.1.2 --- XM2VTS face database --- p.24Chapter 4.1.3 --- MMLAB face database --- p.26Chapter 4.1.4 --- Face Data Preprocessing --- p.27Chapter 4.2 --- Recognition Formulation --- p.29Chapter 4.3 --- PCA-based Recognition Using Different Training Data Sets --- p.29Chapter 4.3.1 --- Experiments on MMLAB Face Database --- p.30Chapter 4.3.1.1 --- Training Data Sets and Testing Data Sets --- p.30Chapter 4.3.1.2 --- Face Recognition Performance Using Different Training Data Sets --- p.31Chapter 4.3.2 --- Experiments on XM2VTS Face Database --- p.33Chapter 4.3.3 --- Comparison of MDEE and KLT --- p.36Chapter 4.3.4 --- Summary --- p.38Chapter 4.4 --- LDA-based Recognition Using Different Training Data Sets --- p.38Chapter 4.4.1 --- Experiments on AR Face Database --- p.38Chapter 4.4.1.1 --- The Selection of Training Data and Testing Data --- p.38Chapter 4.4.1.2 --- LDA-based recognition on AR face database --- p.39Chapter 4.4.2 --- Experiments on XM2VTS Face Database --- p.40Chapter 4.4.3 --- Training Data Sets and Testing Data Sets --- p.41Chapter 4.4.4 --- Experiments on XM2VTS Face Database --- p.42Chapter 4.4.5 --- Summary --- p.46Chapter Chapter 5 --- Summary --- p.47Bibliography --- p.4

Scale-space and wavelet decomposition based scheme for face recognition using nearest linear combination.

Face recognition has attracted much attention from Artificial Intelligence researchers due to its wide acceptability in many applications. Many techniques have been suggested to develop a practical face recognition system that has the ability to handle different challenges. Illumination variation is one of the major issues that significantly affects the performances of face recognition systems. Among many illumination robust approaches, scale-space decomposition based methods play an important role in reducing the lighting effects in facial images. This research presents a face recognition approach for utilizing both the scale-space decomposition and wavelet decomposition methods. In most cases, the existing scale-space decomposition methods perform recognition, based on only the illumination-invariant small-scale features. The proposed approach uses both large-scale and small-scale features through scale-space decomposition and wavelet decomposition. Together with the Nearest Linear Combination (NLC) approach, the proposed system is validated on different databases. The experimental results have shown that the system outperforms many recognition methods in the same category. --Leaf ii.The original print copy of this thesis may be available here: http://wizard.unbc.ca/record=b194710