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Features

Explains the theory and practice of face detection and recognition systems currently in vogue
Offers a general review of the available face detection and recognition methods, as well as an indication of future research using cognitive neurophysiology
Provides a single source for cutting-edge information on the major approaches, algorithms, and technologies used in automated face detection and recognition
Summary

Face detection and recognition are the nonintrusive biometrics of choice in many security applications. Examples of their use include border control, driver's license issuance, law enforcement investigations, and physical access control.

Face Detection and Recognition: Theory and Practice elaborates on and explains the theory and practice of face detection and recognition systems currently in vogue. The book begins with an introduction to the state of the art, offering a general review of the available methods and an indication of future research using cognitive neurophysiology. The text then:

Explores subspace methods for dimensionality reduction in face image processing, statistical methods applied to face detection, and intelligent face detection methods dominated by the use of artificial neural networks
Covers face detection with colour and infrared face images, face detection in real time, face detection and recognition using set estimation theory, face recognition using evolutionary algorithms, and face recognition in frequency domain
Discusses methods for the localization of face landmarks helpful in face recognition, methods of generating synthetic face images using set estimation theory, and databases of face images available for testing and training systems
Features pictorial descriptions of every algorithm as well as downloadable source code (in MATLAB®/PYTHON) and hardware implementation strategies with code examples
Demonstrates how frequency domain correlation techniques can be used supplying exhaustive test results
Face Detection and Recognition: Theory and Practice provides students, researchers, and practitioners with a single source for cutting-edge information on the major approaches, algorithms, and technologies used in automated face detection and recognition.



Table of Contents

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

Perfor