Return to search

A generic face processing framework: technologies, analyses and applications.

Jang Kim-fung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2003. / Includes bibliographical references (leaves 108-124). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background --- p.1 / Chapter 1.2 --- Introduction about Face Processing Framework --- p.4 / Chapter 1.2.1 --- Basic architecture --- p.4 / Chapter 1.2.2 --- Face detection --- p.5 / Chapter 1.2.3 --- Face tracking --- p.6 / Chapter 1.2.4 --- Face recognition --- p.6 / Chapter 1.3 --- The scope and contributions of the thesis --- p.7 / Chapter 1.4 --- The outline of the thesis --- p.8 / Chapter 2 --- Facial Feature Representation --- p.10 / Chapter 2.1 --- Facial feature analysis --- p.10 / Chapter 2.1.1 --- Pixel information --- p.11 / Chapter 2.1.2 --- Geometry information --- p.13 / Chapter 2.2 --- Extracting and coding of facial feature --- p.14 / Chapter 2.2.1 --- Face recognition --- p.15 / Chapter 2.2.2 --- Facial expression classification --- p.38 / Chapter 2.2.3 --- Other related work --- p.44 / Chapter 2.3 --- Discussion about facial feature --- p.48 / Chapter 2.3.1 --- Performance evaluation for face recognition --- p.49 / Chapter 2.3.2 --- Evolution of the face recognition --- p.52 / Chapter 2.3.3 --- Evaluation of two state-of-the-art face recog- nition methods --- p.53 / Chapter 2.4 --- Problem for current situation --- p.58 / Chapter 3 --- Face Detection Algorithms and Committee Ma- chine --- p.61 / Chapter 3.1 --- Introduction about face detection --- p.62 / Chapter 3.2 --- Face Detection Committee Machine --- p.64 / Chapter 3.2.1 --- Review of three approaches for committee machine --- p.65 / Chapter 3.2.2 --- The approach of FDCM --- p.68 / Chapter 3.3 --- Evaluation --- p.70 / Chapter 4 --- Facial Feature Localization --- p.73 / Chapter 4.1 --- Algorithm for gray-scale image: template match- ing and separability filter --- p.73 / Chapter 4.1.1 --- Position of face and eye region --- p.74 / Chapter 4.1.2 --- Position of irises --- p.75 / Chapter 4.1.3 --- Position of lip --- p.79 / Chapter 4.2 --- Algorithm for color image: eyemap and separa- bility filter --- p.81 / Chapter 4.2.1 --- Position of eye candidates --- p.81 / Chapter 4.2.2 --- Position of mouth candidates --- p.83 / Chapter 4.2.3 --- Selection of face candidates by cost function --- p.84 / Chapter 4.3 --- Evaluation --- p.85 / Chapter 4.3.1 --- Algorithm for gray-scale image --- p.86 / Chapter 4.3.2 --- Algorithm for color image --- p.88 / Chapter 5 --- Face Processing System --- p.92 / Chapter 5.1 --- System architecture and limitations --- p.92 / Chapter 5.2 --- Pre-processing module --- p.93 / Chapter 5.2.1 --- Ellipse color model --- p.94 / Chapter 5.3 --- Face detection module --- p.96 / Chapter 5.3.1 --- Choosing the classifier --- p.96 / Chapter 5.3.2 --- Verifying the candidate region --- p.97 / Chapter 5.4 --- Face tracking module --- p.99 / Chapter 5.4.1 --- Condensation algorithm --- p.99 / Chapter 5.4.2 --- Tracking the region using Hue color model --- p.101 / Chapter 5.5 --- Face recognition module --- p.102 / Chapter 5.5.1 --- Normalization --- p.102 / Chapter 5.5.2 --- Recognition --- p.103 / Chapter 5.6 --- Applications --- p.104 / Chapter 6 --- Conclusion --- p.106 / Bibliography --- p.107

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_324303
Date January 2003
ContributorsJang, Kim-fung., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering.
Source SetsThe Chinese University of Hong Kong
LanguageEnglish, Chinese
Detected LanguageEnglish
TypeText, bibliography
Formatprint, x, 124 leaves : ill. ; 30 cm.
RightsUse of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Page generated in 0.0023 seconds