Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble. The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition. Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using real-life scenarios. After reading this book you will be able to apply image processing techniques and make machine learning models for customized application. What You Will LearnDiscover image-processing algorithms and their applications using Python Explore image processing using the OpenCV library Use TensorFlow, scikit-learn, NumPy, and other libraries Work with machine learning and deep learning algorithms for image processing Apply image-processing techniques to five real-time projects Who This Book Is For Data scientists and software developers interested in image processing and computer vision.

More than 30 leading experts from around the world provide comprehensive coverage of various branches of face image analysis, making this text a valuable asset for students, researchers, and practitioners engaged in the study, research, and development of face image analysis techniques.

Advances in Face Image Analysis: Theory and applications describes several approaches to facial image analysis and recognition. Eleven chapters cover advances in computer vision and pattern recognition methods used to analyze facial data. The topics addressed in this book include automatic face detection, 3D face model fitting, robust face recognition, facial expression recognition, face image data embedding, model-less 3D face pose estimation and image-based age estimation. The chapters are also written by experts from a different research groups. Readers will, therefore, have access to contemporary knowledge on facial recognition with some diverse perspectives offered for individual techniques. The book is a useful resource for a wide audience such as i) researchers and professionals working in the field of face image analysis, ii) the entire pattern recognition community interested in processing and extracting features from raw face images, and iii) technical experts as well as postgraduate computer science students interested in cutting edge concepts of facial image recognition.

Doctoral Thesis / Dissertation from the year 2008 in the subject Computer Science - Applied, grade: 1, University of Freiburg (Lehrstuhl für Mustererkennung und Bildverarbeitung), language: English, abstract: 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-dim

Facial recognition software has improved by leaps and bounds over the past few decades, with error rates decreasing significantly within the past ten years. Though this is true, conditions such as poor lighting, obstructions, and profile-only angles have continued to persist in preventing wholly accurate readings. Face Recognition in Adverse Conditions examines how the field of facial recognition takes these adverse conditions into account when designing more effective applications by discussing facial recognition under real world PIE variations, current applications, and the future of the field of facial recognition research. The work is intended for academics, engineers, and researchers specializing in the field of facial recognition.

This book seeks to comprehensively address the face recognition problem while gaining new insights from complementary fields of endeavor. These include neurosciences, statistics, signal and image processing, computer vision, machine learning and data mining. The book examines the evolution of research surrounding the field to date, explores new directions, and offers specific guidance on the most promising venues for future research and development. The book’s focused approach and its clarity of presentation make this an excellent reference work.