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| Author | : Yuechao Zhao |
| Publisher | : |
| Total Pages | : 324 |
| Release | : 2003 |
| Genre | : |
| ISBN | : |
| Author | : A. Shenoy |
| Publisher | : |
| Total Pages | : |
| Release | : 2010 |
| Genre | : |
| ISBN | : |
This PhD work constitutes a series of inter-disciplinary studies that use biologically plausible computational techniques and experiments with human subjects in analyzing facial expressions. The performance of the computational models and human subjects in terms of accuracy and response time are analyzed. The computational models process images in three stages. This includes: Preprocessing, dimensionality reduction and Classification. The pre-processing of face expression images includes feature extraction and dimensionality reduction. Gabor filters are used for feature extraction as they are closest biologically plausible computational method. Various dimensionality reduction methods: Principal Component Analysis (PCA), Curvilinear Component Analysis (CCA) and Fisher Linear Discriminant (FLD) are used followed by the classification by Support Vector Machines (SVM) and Linear Discriminant Analysis (LDA). Six basic prototypical facial expressions that are universally accepted are used for the analysis. They are: angry, happy, fear, sad, surprise and disgust. The performance of the computational models in classifying each expression category is compared with that of the human subjects. The Effect size and Encoding face enable the discrimination of the areas of the face specific for a particular expression. The Effect size in particular emphasizes the areas of the face that are involved during the production of an expression. This concept of using Effect size on faces has not been reported previously in the literature and has shown very interesting results. The detailed PCA analysis showed the significant PCA components specific for each of the six basic prototypical expressions. An important observation from this analysis was that with Gabor filtering followed by non linear CCA for dimensionality reduction, the dataset vector size may be reduced to a very small number, in most cases it was just 5 components. The hypothesis that the average response time (RT) for the human subjects in classifying the different expressions is analogous to the distance measure of the data points from the classification hyper-plane was verified. This means the harder a facial expression is to classify by human subjects, the closer to the classifying hyper-plane of the classifier it is. A bi-variate correlation analysis of the distance measure and the average RT suggested a significant anti-correlation. The signal detection theory (SDT) or the d-prime determined how well the model or the human subjects were in making the classification of an expressive face from a neutral one. On comparison, human subjects are better in classifying surprise, disgust, fear, and sad expressions. The RAW computational model is better able to distinguish angry and happy expressions. To summarize, there seems to some similarities between the computational models and human subjects in the classification process.
| Author | : Rafael A. Calvo |
| Publisher | : Oxford Library of Psychology |
| Total Pages | : 625 |
| Release | : 2015 |
| Genre | : Computers |
| ISBN | : 0199942234 |
"The Oxford Handbook of Affective Computing is a definitive reference in the burgeoning field of affective computing (AC), a multidisciplinary field encompassing computer science, engineering, psychology, education, neuroscience, and other disciplines. AC research explores how affective factors influence interactions between humans and technology, how affect sensing and affect generation techniques can inform our understanding of human affect, and on the design, implementation, and evaluation of systems involving affect at their core. The volume features 41 chapters and is divided into five sections: history and theory, detection, generation, methodologies, and applications. Section 1 begins with the making of AC and a historical review of the science of emotion. The following chapters discuss the theoretical underpinnings of AC from an interdisciplinary viewpoint. Section 2 examines affect detection or recognition, a commonly investigated area. Section 3 focuses on aspects of affect generation, including the synthesis of emotion and its expression via facial features, speech, postures, and gestures. Cultural issues are also discussed. Section 4 focuses on methodological issues in AC research, including data collection techniques, multimodal affect databases, formats for the representation of emotion, crowdsourcing techniques, machine learning approaches, affect elicitation techniques, useful AC tools, and ethical issues. Finally, Section 5 highlights applications of AC in such domains as formal and informal learning, games, robotics, virtual reality, autism research, health care, cyberpsychology, music, deception, reflective writing, and cyberpsychology. This compendium will prove suitable for use as a textbook and serve as a valuable resource for everyone with an interest in AC."--
| Author | : S. Kevin Zhou |
| Publisher | : Springer |
| Total Pages | : 314 |
| Release | : 2007-11-04 |
| Genre | : Computers |
| ISBN | : 3540756906 |
This book constitutes the refereed proceedings of the Third International Workshop on Analysis and Modelling of Faces and Gestures, AMFG 2007, held within the scope of ICCV 2007, the International Conference on Computer Vision. The papers review the status of recognition, analysis and modeling of face, gesture, activity, and behavior. Topics addressed include feature representation, 3D face, video-based face recognition, facial motion analysis, and sign recognition.
| Author | : Yi Li |
| Publisher | : Springer Nature |
| Total Pages | : 104 |
| Release | : 2020-06-24 |
| Genre | : Computers |
| ISBN | : 9811391483 |
This book presents a comprehensive review of heterogeneous face analysis and synthesis, ranging from the theoretical and technical foundations to various hot and emerging applications, such as cosmetic transfer, cross-spectral hallucination and face rotation. Deep generative models have been at the forefront of research on artificial intelligence in recent years and have enhanced many heterogeneous face analysis tasks. Not only has there been a constantly growing flow of related research papers, but there have also been substantial advances in real-world applications. Bringing these together, this book describes both the fundamentals and applications of heterogeneous face analysis and synthesis. Moreover, it discusses the strengths and weaknesses of related methods and outlines future trends. Offering a rich blend of theory and practice, the book represents a valuable resource for students, researchers and practitioners who need to construct face analysis systems with deep generative networks.
| Author | : Yuyao Zhang |
| Publisher | : |
| Total Pages | : 175 |
| Release | : 2020 |
| Genre | : |
| ISBN | : |
Face analysis techniques commonly require a proper representation of images by means of dimensionality reduction leading to embedded manifolds, which aims at capturing relevant characteristics of the signals. In this thesis, we first provide a comprehensive survey on the state of the art of embedded manifold models. Then, we introduce a novel non-linear embedding method, the Kernel Similarity Principal Component Analysis (KS-PCA), into Active Appearance Models, in order to model face appearances under variable illumination. The proposed algorithm successfully outperforms the traditional linear PCA transform to capture the salient features generated by different illuminations, and reconstruct the illuminated faces with high accuracy. We also consider the problem of automatically classifying human face poses from face views with varying illumination, as well as occlusion and noise. Based on the sparse representation methods, we propose two dictionary-learning frameworks for this pose classification problem. The first framework is the Adaptive Sparse Representation pose Classification (ASRC). It trains the dictionary via a linear model called Incremental Principal Component Analysis (Incremental PCA), tending to decrease the intra-class redundancy which may affect the classification performance, while keeping the extra-class redundancy which is critical for sparse representation. The other proposed work is the Dictionary-Learning Sparse Representation model (DLSR) that learns the dictionary with the aim of coinciding with the classification criterion. This training goal is achieved by the K-SVD algorithm. In a series of experiments, we show the performance of the two dictionary-learning methods which are respectively based on a linear transform and a sparse representation model. Besides, we propose a novel Dictionary Learning framework for Illumination Normalization (DL-IN). DL-IN based on sparse representation in terms of coupled dictionaries. The dictionary pairs are jointly optimized from normally illuminated and irregularly illuminated face image pairs. We further utilize a Gaussian Mixture Model (GMM) to enhance the framework's capability of modeling data under complex distribution. The GMM adapt each model to a part of the samples and then fuse them together. Experimental results demonstrate the effectiveness of the sparsity as a prior for patch-based illumination normalization for face images.
| Author | : Hassan Ugail |
| Publisher | : Springer |
| Total Pages | : 61 |
| Release | : 2019-04-17 |
| Genre | : Computers |
| ISBN | : 3030153819 |
In this book, the authors discuss the recent developments in computational techniques for automated non-invasive facial emotion detection and analysis with particular focus on the smile. By way of applications, they discuss how genuine and non-genuine smiles can be inferred, how gender is encoded in a smile and how it is possible to use the dynamics of a smile itself as a biometric feature. It is often said that the face is a window to the soul. Bearing a metaphor of this nature in mind, one might find it intriguing to understand, if any, how the physical, behavioural as well as emotional characteristics of a person could be decoded from the face itself. With the increasing deductive power of machine learning techniques, it is becoming plausible to address such questions through the development of appropriate computational frameworks. Though there are as many as over twenty five categories of emotions one could express, regardless of the ethnicity, gender or social class, across humanity, there exist six common emotions – namely happiness, sadness, surprise, fear, anger and disgust - all of which can be inferred from facial expressions. Of these facial expressions, the smile is the most prominent in social interactions. The smile bears important ramifications with beliefs such as it makes one more attractive, less stressful in upsetting situations and employers tending to promote people who smile often. Even pockets of scientific research appear to be forthcoming to validate such beliefs and claims, e.g. the smile intensity observed in photographs positively correlates with longevity, the ability to win a fight and whether a couple would stay married. Thus, it appears that many important personality traits are encoded in the smile itself. Therefore, the deployment of computer based algorithms for studying the human smiles in greater detail is a plausible avenue for which the authors have dedicated the discussions in this book.
| Author | : Mark T. Maybury |
| Publisher | : John Wiley & Sons |
| Total Pages | : 436 |
| Release | : 2012-07-11 |
| Genre | : Computers |
| ISBN | : 111821952X |
The advent of increasingly large consumer collections of audio (e.g., iTunes), imagery (e.g., Flickr), and video (e.g., YouTube) is driving a need not only for multimedia retrieval but also information extraction from and across media. Furthermore, industrial and government collections fuel requirements for stock media access, media preservation, broadcast news retrieval, identity management, and video surveillance. While significant advances have been made in language processing for information extraction from unstructured multilingual text and extraction of objects from imagery and video, these advances have been explored in largely independent research communities who have addressed extracting information from single media (e.g., text, imagery, audio). And yet users need to search for concepts across individual media, author multimedia artifacts, and perform multimedia analysis in many domains. This collection is intended to serve several purposes, including reporting the current state of the art, stimulating novel research, and encouraging cross-fertilization of distinct research disciplines. The collection and integration of a common base of intellectual material will provide an invaluable service from which to teach a future generation of cross disciplinary media scientists and engineers.
| Author | : Antonio J. Colmenarez |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 150 |
| Release | : 2005-12-17 |
| Genre | : Computers |
| ISBN | : 140207803X |
Computer vision algorithms for the analysis of video data are obtained from a camera aimed at the user of an interactive system. It is potentially useful to enhance the interface between users and machines. These image sequences provide information from which machines can identify and keep track of their users, recognize their facial expressions and gestures, and complement other forms of human-computer interfaces. Facial Analysis from Continuous Video with Applications to Human-Computer Interfaces presents a learning technique based on information-theoretic discrimination which is used to construct face and facial feature detectors. This book also describes a real-time system for face and facial feature detection and tracking in continuous video. Finally, this book presents a probabilistic framework for embedded face and facial expression recognition from image sequences. Facial Analysis from Continuous Video with Applications to Human-Computer Interfaces is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a secondary text for graduate-level students in computer science and engineering.
| Author | : Shaogang Gong |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 433 |
| Release | : 2005-10-04 |
| Genre | : Computers |
| ISBN | : 3540292292 |
This book constitutes the refereed proceedings of the Second International Workshop on Analysis and Modelling of Faces and Gestures, AMFG 2005, held in Beijing, China in October 2005 within the scope of ICCV 2005, the International Conference on Computer Vision. The 30 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 90 submissions. The papers give a survey of the status of recognition, analysis and modeling of face and gesture. The topics of these papers range from feature representation, robust recognition, learning, 3D modeling, to psychology.
