Face recognition committee machine: methodology, experiments, and a system application.

Tang Ho-Man.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 85-92).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Face Recognition --- p.2Chapter 1.3 --- Contributions --- p.4Chapter 1.4 --- Organization of this Thesis --- p.6Chapter 2 --- Literature Review --- p.8Chapter 2.1 --- Committee Machine --- p.8Chapter 2.1.1 --- Static Structure --- p.9Chapter 2.1.2 --- Dynamic Structure --- p.10Chapter 2.2 --- Face Recognition Algorithms Overview --- p.11Chapter 2.2.1 --- Eigenface --- p.12Chapter 2.2.2 --- Fisherface --- p.17Chapter 2.2.3 --- Elastic Graph Matching --- p.19Chapter 2.2.4 --- Support Vector Machines --- p.23Chapter 2.2.5 --- Neural Networks --- p.25Chapter 2.3 --- Commercial System and Applications --- p.27Chapter 2.3.1 --- FaceIT --- p.28Chapter 2.3.2 --- ZN-Face --- p.28Chapter 2.3.3 --- TrueFace --- p.29Chapter 2.3.4 --- Viisage --- p.30Chapter 3 --- Static Structure --- p.31Chapter 3.1 --- Introduction --- p.31Chapter 3.2 --- Architecture --- p.32Chapter 3.3 --- Result and Confidence --- p.33Chapter 3.3.1 --- "Eigenface, Fisherface, EGM" --- p.34Chapter 3.3.2 --- SVM --- p.35Chapter 3.3.3 --- Neural Networks --- p.36Chapter 3.4 --- Weight --- p.37Chapter 3.5 --- Voting Machine --- p.38Chapter 4 --- Dynamic Structure --- p.40Chapter 4.1 --- Introduction --- p.40Chapter 4.2 --- Architecture --- p.41Chapter 4.3 --- Gating Network --- p.42Chapter 4.4 --- Feedback Mechanism --- p.44Chapter 5 --- Face Recognition System --- p.46Chapter 5.1 --- Introduction --- p.46Chapter 5.2 --- System Architecture --- p.47Chapter 5.2.1 --- Face Detection Module --- p.48Chapter 5.2.2 --- Face Recognition Module --- p.49Chapter 5.3 --- Face Recognition Process --- p.50Chapter 5.3.1 --- Enrollment --- p.51Chapter 5.3.2 --- Recognition --- p.52Chapter 5.4 --- Distributed System --- p.54Chapter 5.4.1 --- Problems --- p.55Chapter 5.4.2 --- Distributed Architecture --- p.56Chapter 5.5 --- Conclusion --- p.59Chapter 6 --- Experimental Results --- p.60Chapter 6.1 --- Introduction --- p.60Chapter 6.2 --- Database --- p.61Chapter 6.2.1 --- ORL Face Database --- p.61Chapter 6.2.2 --- Yale Face Database --- p.62Chapter 6.2.3 --- AR Face Database --- p.62Chapter 6.2.4 --- HRL Face Database --- p.63Chapter 6.3 --- Experimental Details --- p.64Chapter 6.3.1 --- Pre-processing --- p.64Chapter 6.3.2 --- Cross Validation --- p.67Chapter 6.3.3 --- System details --- p.68Chapter 6.4 --- Result --- p.69Chapter 6.4.1 --- ORL Result --- p.69Chapter 6.4.2 --- Yale Result --- p.72Chapter 6.4.3 --- AR Result --- p.73Chapter 6.4.4 --- HRL Result --- p.75Chapter 6.4.5 --- Average Running Time --- p.76Chapter 6.5 --- Discussion --- p.77Chapter 6.5.1 --- Advantages --- p.78Chapter 6.5.2 --- Disadvantages --- p.79Chapter 6.6 --- Conclusion --- p.80Chapter 7 --- Conclusion --- p.82Bibliography --- p.9

New radial basis function network based techniques for holistic recognition of facial expressions

Ph.DDOCTOR OF PHILOSOPH

State of the Art in Face Recognition

Notwithstanding the tremendous effort to solve the face recognition problem, it is not possible yet to design a face recognition system with a potential close to human performance. New computer vision and pattern recognition approaches need to be investigated. Even new knowledge and perspectives from different fields like, psychology and neuroscience must be incorporated into the current field of face recognition to design a robust face recognition system. Indeed, many more efforts are required to end up with a human like face recognition system. This book tries to make an effort to reduce the gap between the previous face recognition research state and the future state

Evaluation and analysis of hybrid intelligent pattern recognition techniques for speaker identification

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content (i.e. text-independent), and to design efficient methods of combining face and voice in producing a robust authentication system. A novel approach towards speaker identification is developed using wavelet analysis, and multiple neural networks including Probabilistic Neural Network (PNN), General Regressive Neural Network (GRNN)and Radial Basis Function-Neural Network (RBF NN) with the AND voting scheme. This approach is tested on GRID and VidTIMIT cor-pora and comprehensive test results have been validated with state- of-the-art approaches. The system was found to be competitive and it improved the recognition rate by 15% as compared to the classical Mel-frequency Cepstral Coe±cients (MFCC), and reduced the recognition time by 40% compared to Back Propagation Neural Network (BPNN), Gaussian Mixture Models (GMM) and Principal Component Analysis (PCA). Another novel approach using vowel formant analysis is implemented using Linear Discriminant Analysis (LDA). Vowel formant based speaker identification is best suitable for real-time implementation and requires only a few bytes of information to be stored for each speaker, making it both storage and time efficient. Tested on GRID and Vid-TIMIT, the proposed scheme was found to be 85.05% accurate when Linear Predictive Coding (LPC) is used to extract the vowel formants, which is much higher than the accuracy of BPNN and GMM. Since the proposed scheme does not require any training time other than creating a small database of vowel formants, it is faster as well. Furthermore, an increasing number of speakers makes it di±cult for BPNN and GMM to sustain their accuracy, but the proposed score-based methodology stays almost linear. Finally, a novel audio-visual fusion based identification system is implemented using GMM and MFCC for speaker identi¯cation and PCA for face recognition. The results of speaker identification and face recognition are fused at different levels, namely the feature, score and decision levels. Both the score-level and decision-level (with OR voting) fusions were shown to outperform the feature-level fusion in terms of accuracy and error resilience. The result is in line with the distinct nature of the two modalities which lose themselves when combined at the feature-level. The GRID and VidTIMIT test results validate that the proposed scheme is one of the best candidates for the fusion of face and voice due to its low computational time and high recognition accuracy

A Survey of Face Recognition

Recent years witnessed the breakthrough of face recognition with deep convolutional neural networks. Dozens of papers in the field of FR are published every year. Some of them were applied in the industrial community and played an important role in human life such as device unlock, mobile payment, and so on. This paper provides an introduction to face recognition, including its history, pipeline, algorithms based on conventional manually designed features or deep learning, mainstream training, evaluation datasets, and related applications. We have analyzed and compared state-of-the-art works as many as possible, and also carefully designed a set of experiments to find the effect of backbone size and data distribution. This survey is a material of the tutorial named The Practical Face Recognition Technology in the Industrial World in the FG2023

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

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

QUEST Hierarchy for Hyperspectral Face Recognition

Face recognition is an attractive biometric due to the ease in which photographs of the human face can be acquired and processed. The non-intrusive ability of many surveillance systems permits face recognition applications to be used in a myriad of environments. Despite decades of impressive research in this area, face recognition still struggles with variations in illumination, pose and expression not to mention the larger challenge of willful circumvention. The integration of supporting contextual information in a fusion hierarchy known as QUalia Exploitation of Sensor Technology (QUEST) is a novel approach for hyperspectral face recognition that results in performance advantages and a robustness not seen in leading face recognition methodologies. This research demonstrates a method for the exploitation of hyperspectral imagery and the intelligent processing of contextual layers of spatial, spectral, and temporal information. This approach illustrates the benefit of integrating spatial and spectral domains of imagery for the automatic extraction and integration of novel soft features (biometric). The establishment of the QUEST methodology for face recognition results in an engineering advantage in both performance and efficiency compared to leading and classical face recognition techniques. An interactive environment for the testing and expansion of this recognition framework is also provided