책 이미지
책 정보
· 분류 : 외국도서 > 컴퓨터 > 컴퓨터 비전/패턴 인식
· ISBN : 9781482226546
· 쪽수 : 352쪽
· 출판일 : 2015-11-20
목차
Introduction
Introduction
Biometric identity authentication techniques
Face as biometric identity
Automated face recognition system
Process flow in face recognition system
Problems of face identification and recognition techniques
Liveness detection for face recognition
Tests and metrics
Cognitive psychology in face recognition
Face detection and recognition techniques
Introduction to face detection
Feature based approaches for face detection
Low level analysis
Active shape model
Feature analysis
Image based approaches for face detection
Statistical approaches
Face recognition methods
Geometric feature based method
Subspace based face recognition
Neural network based face recognition
Correlation based method
Matching pursuit based methods
Support vector machine approach
Selected works on face classifiers
Face reconstruction techniques
Three dimensional face recognition
Subspace based face recognition
Introduction
Principal component analysis
Two dimensional principal component analysis
Kernel principal component analysis
Fisher linear discriminant analysis
Fisher linear discriminant analysis for two class case
Independent component analysis
Face detection by Bayesian approach
Introduction
Bayes decision rule for classification
Gaussian distribution
Bayes theorem
Bayesian decision boundaries and discriminant function
Density estimation using eigenspace decomposition
Bayesian discriminant features (BDF) method for face detection
Modelling of face and non-face pattern
Bayes classification using BDF
Experiments and results
Face detection in colour and infrared images
Introduction
Face detection in colour images
Colour spaces
RGB model
HSI colour model
YCbCr colour space
Face detection from skin regions
Skin modelling
Probabilistic skin detection
Face detection by localizing facial features
Eye map
Mouth map
Face detection in infrared images
Multivariate histogram based image segmentation
Method for finding major clusters from a multivariate histogram
Experiments and results on the colour and IR face image datasets
Utility of facial features
Intelligent face detection
Introduction
Multilayer perceptron model
Learning algorithm
Face detection networks
Training images
Data preparation
Face training
Exhaustive training
Evaluation of face detection for upright faces
Algorithm
Image scanning and face detection
Real time face detection
Introduction
Features
Integral image
Rectangular feature calculation from integral image
ADABOOST
Modified ADABOOST algorithm
Cascade classifier
Face detection using OpenCV
Face space boundary selection for face detection and recognition
Introduction
Face points, face classes and face space boundaries for face detection and recognition
Mathematical preliminaries for set estimation method
Face space boundary selection using set estimation for face detection
Algorithm for global threshold based face detection
Experimental design and result analysis
Face / non-face classification using global threshold during face detection
Comparison between threshold selections by ROC based and set estimation based techniques
Classification of face / non-face regions of an image containing multiple faces
Class specific thresholds of face-class boundaries for face recognition
Experimental design and result analysis on face datasets for face recognition
Description of face dataset
Open test results considering imposters in the system
Recognition rates considering only clients in the system
Evolutionary design for face recognition
Introduction
Genetic algorithms
Implementation
Algorithm
Representation and discrimination
Whitening and rotation transformation
Chromosome representation and genetic operators
The fitness function
The evolutionary pursuit algorithm for face recognition
Frequency domain correlation filters in face recognition
Introduction
PSR calculation
A brief review on correlation filters
Mathematical background of a representative correlation filter
ECPSDF filter design
MACE filter design
MVSDF filter design
Optimal tradeoff (OTF) filter design
Unconstrained correlation filter design
UMACE filter design
OTMACH filter design
Physical requirements in designing correlation filters
Applications of correlation filter in face recognition
Performance analysis of correlation filters in face recognition
Performance evaluation using PSR values
Performance evaluation in terms of %RR and %FAR
Performance evaluation by receiver operating characteristics (ROC) curves
Correlation filters for face detection and recognition in video
Formulation of unconstrained video filter
Mathematical formulation of MUOTSDF
Unconstrained video filter
Distance classifier correlation filter
Application of UVF in video for face detection
Training approach
Testing approach
Face detection in video using UVF
Validation of face detection
Face classification using DCCF
Subspace based face recognition in frequency domain
Introduction
Subspace based correlation filter
Mathematical modelling of 1D subspace based correlation filters
Reconstructed correlation filter using 1D subspace
Optimum projecting image correlation filter using 1D subspace
Face classification and recognition analysis in frequency domain
Test results with 1D subspace analysis
Comparative study in terms of PSRs
Comparative study on %RR and %FAR
Mathematical modelling of 2D subspace based correlation filter
Reconstructed correlation filter using 2D subspace
Test results on 2D subspace analysis
PSR value distribution for authentic and impostors
Comparative performance in terms of %RR
Performance evaluation using ROC analysis
Class specific subspace based nonlinear correlation filter
Formulation of nonlinear correlation filters
Nonlinear optimum projecting image correlation filter
Nonlinear optimum reconstructed image correlation filter
Face recognition analysis using correlation classifiers
Test results
Comparative study on discriminating performances
Comparative performance based on PSR distribution
Performance analysis using ROC
Noise sensitivity
Landmark localization for face recognition
Introduction
Elastic bunch graph matching
Gabor Wavelets
Gabor Jets
The EBGM algorithm
Application to face recognition
Application of frequency domain correlation filter in facial landmark detection
ASEF correlation filter
Formulation of ASEF
Eye detection
Multi correlation approach using landmark filter for facial landmark detection
Design of landmark filter (LF)
Landmark localization with localization filter
Test results
Two dimensional synthetic face generation using set estimation technique
Introduction
Generating face points in the face space taking the features from intra class face images
Face generation using algorithm with intra class features and related peak signal to noise ratio
Generating face points in the face space taking the features from inter class face images
Face generation with inter class features
Rejection of the non-meaningful face and corresponding PSNR test
Generalization capability of set estimation method: generating face images not in training set
Test of significance
Datasets of face images and performance tests for face recognition
Face datasets
ORL dataset
OULU physics dataset
XM2VTS dataset
YALE dataset
Yale-B dataset
MIT dataset
CMU pose, illumination, and expression (PIE) dataset
UMIST dataset
PURDU AR dataset
FERET dataset
Performance evaluation of face recognition algorithms
FERET and XM2VTS protocols
Face recognition grand challenge - FRGC
Face recognition vendor test - FRVT
Multiple biometric grand challenge
Focus of evaluation
Conclusion
