In this work, we present the problem of automatic appearance-based facial analysis with machine learning techniques and describe common specific sub-problems like face detection, facial feature detection and face recognition which are the crucial parts of many applications in the context of indexation, surveillance, access-control or human-computer interaction.
To tackle this problem, we particularly focus on a technique called Convolutional Neural Network (CNN) which is inspired by biological evidence found in the visual cortex of mammalian brains and which has already been applied to many different classi
fication problems. Existing CNN-based methods, like the
face detection system proposed by Garcia and Delakis, show that this can be a very effective, efficient and robust approach to non-linear image processing tasks.
An important step in many automatic facial analysis applications, e.g. face recognition, is face alignment which tries to translate, scale and rotate the face image such that specific facial features are roughly at predefined positions in the image. We propose an efficient approach to this problem using CNNs and experimentally show its very good performance on difficult test images.
We further present a CNN-based method for automatic facial feature detection. The proposed system employs a hierarchical procedure which first roughly localizes the eyes, the nose and the mouth and then refines the result by detecting 10 different facial feature points. The detection rate of this method is 96%
for the AR database and 87% for the BioID database tolerating an error of 10% of the inter-ocular distance.
Finally, we propose a novel face recognition approach based on a specific CNN architecture learning a non-linear mapping of the image space into a lower-dimensional sub-space where the different classes are more easily separable.
We applied this method to several public face databases and obtained better recognition rates than with classical face recognition approaches based on PCA or LDA.
We also present a CNN-based method for the binary classification problem of gender recognition with face images and achieve a state-of-the-art accuracy.
The results presented in this work show that CNNs perform very well on various facial image processing tasks, such as face alignment, facial feature detection and face recognition and clearly demonstrate that the CNN technique is a versatile, efficient and robust approach for facial image analysis.
Table of Contents
1 Introduction
1.1 Context
1.2 Applications
1.3 Difficulties
1.3.1 Illumination
1.3.2 Pose
1.3.3 Facial Expressions
1.3.4 Partial Occlusions
1.3.5 Other types of variations
1.4 Objectives
1.5 Outline
2 Machine Learning Techniques for Object Detection and Recognition
2.1 Introduction
2.2 Statistical Projection Methods
2.2.1 Principal Component Analysis
2.2.2 Linear Discriminant Analysis
2.2.3 Other Projection Methods
2.3 Active Appearance Models
2.3.1 Modeling shape and appearance
2.3.2 Matching the model
2.4 Hidden Markov Models
2.4.1 Introduction
2.4.2 Finding the most likely state sequence
2.4.3 Training
2.4.4 HMMs for Image Analysis
2.5 Adaboost
2.5.1 Introduction
2.5.2 Training
2.6 Support Vector Machines
2.6.1 Structural Risk Minimization
2.6.2 Linear Support Vector Machines
2.6.3 Non-linear Support Vector Machines
2.6.4 Extension to multiple classes
2.7 Bag of Local Signatures
2.8 Neural Networks
2.8.1 Introduction
2.8.2 Perceptron
2.8.3 Multi-Layer Perceptron
2.8.4 Auto-Associative Neural Networks
2.8.5 Training Neural Networks
2.8.6 Radial Basis Function Networks
2.8.7 Self-Organizing Maps
2.9 Conclusion
3 Convolutional Neural Networks
3.1 Introduction
3.2 Background
3.2.1 Neocognitron
3.2.2 LeCun’s Convolutional Neural Network model
3.3 Training Convolutional Neural Networks
3.3.1 Error Backpropagation with Convolutional Neural Networks
3.3.2 Other training algorithms proposed in the literature
3.4 Extensions and variants
3.4.1 LeNet-5
3.4.2 Space Displacement Neural Networks
3.4.3 Siamese CNNs
3.4.4 Shunting Inhibitory Convolutional Neural Networks
3.4.5 Sparse Convolutional Neural Networks
3.5 Some Applications
3.6 Conclusion
4 Face detection and normalization
4.1 Introduction
4.2 Face detection
4.2.1 Introduction
4.2.2 State-of-the-art
4.2.3 Convolutional Face Finder
4.3 Illumination Normalization
4.4 Pose Estimation
4.5 Face Alignment
4.5.1 Introduction
4.5.2 State-of-the-art
4.5.3 Face Alignment with Convolutional Neural Networks
4.6 Conclusion
5 Facial Feature Detection
5.1 Introduction
5.2 State-of-the-art
5.3 Facial Feature Detection with Convolutional Neural Networks
5.3.1 Introduction
5.3.2 Architecture of the Facial Feature Detection System
5.3.3 Training the Facial Feature Detectors
5.3.4 Facial Feature Detection Procedure
5.3.5 Experimental Results
5.4 Conclusion
6 Face and Gender Recognition
6.1 Introduction
6.2 State-of-the-art in Face Recognition
6.3 Face Recognition with Convolutional Neural Networks
6.3.1 Introduction
6.3.2 Neural Network Architecture
6.3.3 Training Procedure
6.3.4 Recognizing Faces
6.3.5 Experimental Results
6.4 Gender Recognition
6.4.1 Introduction
6.4.2 State-of-the-art
6.4.3 Gender Recognition with Convolutional Neural Networks
6.5 Conclusion
7 Conclusion and Perspectives
7.1 Conclusion
7.2 Perspectives
7.2.1 Convolutional Neural Networks
7.2.2 Facial analysis with Convolutional Neural Networks
Objectives and Research Themes
The principal objective of this research is to evaluate the utility and performance of Convolutional Neural Networks (CNNs) in the context of appearance-based facial analysis, focusing on their robustness to real-world conditions like illumination, pose variations, and partial occlusions.
- Automatic face detection and alignment using CNN-based models.
- Hierarchical facial feature detection in unconstrained images.
- Robust face recognition through non-linear mapping and image reconstruction.
- Gender classification based on CNN architectures.
- Evaluation of CNN performance versus traditional machine learning approaches.
Excerpt from the Book
1.1 Context
The automatic processing of images to extract semantic content is a task that has gained a lot of importance during the last years due to the constantly increasing number of digital photographs on the Internet or being stored on personal home computers. The need to organize them automatically in a intelligent way using indexing and image retrieval techniques requires effective and efficient image analysis and pattern recognition algorithms that are capable to extract relevant semantic information.
Especially faces contain a great deal of valuable information compared to other objects or visual items in images. For example, recognizing a person on a photograph, in general, tells a lot about the overall content of the picture.
In the context of human-computer interaction (HCI), it might also be important to detect the position of specific facial characteristics or recognize facial expressions, in order to allow, for example, a more intuitive communication between the device and the user or to efficiently encode and transmit facial images coming from a camera. Thus, the automatic analysis of face images is crucial for many applications involving visual content retrieval or extraction.
Summary of Chapters
1 Introduction: Provides the context of automatic face image analysis, details potential applications, and outlines the primary research objectives.
2 Machine Learning Techniques for Object Detection and Recognition: Reviews standard machine learning methods, including statistical projections, SVMs, and neural networks, laying the groundwork for CNNs.
3 Convolutional Neural Networks: Discusses the architecture, training processes, and variations of CNNs, highlighting their benefits for image-based tasks.
4 Face detection and normalization: Covers face localization and alignment techniques, introducing the Convolutional Face Finder and a CNN-based alignment system.
5 Facial Feature Detection: Presents a hierarchical approach for identifying specific facial features such as eyes, nose, and mouth using CNNs.
6 Face and Gender Recognition: Explores CNN-based methods for identity and gender recognition, evaluating their effectiveness compared to traditional classification techniques.
7 Conclusion and Perspectives: Summarizes the contributions of the work and suggests future research directions, such as processing multi-modal data.
Keywords
Convolutional Neural Networks, CNN, Face Detection, Face Alignment, Facial Feature Detection, Face Recognition, Gender Recognition, Machine Learning, Pattern Recognition, Image Analysis, Backpropagation, Feature Extraction, Neural Networks, Computer Vision.
Frequently Asked Questions
What is the core focus of this dissertation?
The work focuses on the development and evaluation of Convolutional Neural Networks (CNNs) for various facial analysis tasks, including face detection, alignment, feature localization, and identification.
Which specific facial analysis problems are addressed?
The dissertation addresses face detection, face normalization (alignment), facial feature detection, identity recognition, and gender classification.
What is the primary scientific goal?
The goal is to demonstrate that CNNs provide a versatile, efficient, and robust solution for complex facial image processing tasks in real-world, unconstrained scenarios.
Which machine learning methodology is utilized?
The research relies on supervised learning using Convolutional Neural Networks, trained primarily via variants of the error Backpropagation algorithm.
What does the main part of the thesis cover?
The main part encompasses an extensive review of general machine learning techniques, followed by chapters dedicated to specific CNN architectures for detection, alignment, and recognition tasks.
How is the robustness of the proposed methods evaluated?
Robustness is tested against real-world challenges such as partial occlusions, varying illumination, changes in head pose, and pixel-level noise.
Why are CNNs used for face recognition?
CNNs are used because they learn non-linear mappings and feature extractors automatically, which is superior to manually designed filters when dealing with high-dimensional image data.
How does the proposed face alignment system work?
The system uses a CNN to predict transformation parameters (translation, rotation, scale) for a detected bounding box and iteratively refines these parameters until the face is properly aligned.
- Quote paper
- Dr. Stefan Duffner (Author), 2008, Face Image Analysis with Convolutional Neural Networks, Munich, GRIN Verlag, https://www.grin.com/document/133318
