Automatic Facial Expression Analysis With Data Driven Methods
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Automatic Facial Expression Analysis With Data Driven Methods
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Author : Beat Fasel
language : en
Publisher:
Release Date : 2004
Automatic Facial Expression Analysis With Data Driven Methods written by Beat Fasel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004 with categories.
Data Driven Methods For The Recognition And Synthesis Of Facial Motion
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Author :
language : en
Publisher:
Release Date : 2012
Data Driven Methods For The Recognition And Synthesis Of Facial Motion written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012 with categories.
Data Driven Facial Expression Analysis From Live Video A Thesis Submitted To The Victoria University Of Wellington In Fulfilment Of The Requirements For The Degree Of Master Of Science In Computer Graphics
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Author : Wee Kiat Tay
language : en
Publisher:
Release Date : 2017
Data Driven Facial Expression Analysis From Live Video A Thesis Submitted To The Victoria University Of Wellington In Fulfilment Of The Requirements For The Degree Of Master Of Science In Computer Graphics written by Wee Kiat Tay and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with Computer vision categories.
Emotion analytics is the study of human behavior by analyzing the responses when humans experience different emotions. In this thesis, we research into emotion analytics solutions using computer vision to detect emotions from facial expressions automatically using live video. Considering anxiety is an emotion that can lead to more serious conditions like anxiety disorders and depression, we propose 2 hypotheses to detect anxiety from facial expressions. One hypothesis is that the complex emotion "anxiety" is a subset of the basic emotion "fear". The other hypothesis is that anxiety can be distinguished from fear by differences in head and eye motion. We test the first hypothesis by implementing a basic emotions detector based on facial action coding system (FACS) to detect fear from videos of anxious faces. When we discover that this is not as accurate as we would like, an alternative solution based on Gabor filters is implemented. A comparison is done between the solutions and the Gabor-based solution is found to be inferior. The second hypothesis is tested by using scatter graphs and statistical analysis of the head and eye motions of videos for fear and anxiety expressions. It is found that head pitch has significant differences between fear and anxiety. As a conclusion to the thesis, we implement a systems software using the basic emotions detector based on FACS and evaluate the software by comparing commercials using emotions detected from facial expressions of viewers.
3d Face Modeling Analysis And Recognition
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Author : Mohamed Daoudi
language : en
Publisher: John Wiley & Sons
Release Date : 2013-06-11
3d Face Modeling Analysis And Recognition written by Mohamed Daoudi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-06-11 with Technology & Engineering categories.
3D Face Modeling, Analysis and Recognition presents methodologies for analyzing shapes of facial surfaces, develops computational tools for analyzing 3D face data, and illustrates them using state-of-the-art applications. The methodologies chosen are based on efficient representations, metrics, comparisons, and classifications of features that are especially relevant in the context of 3D measurements of human faces. These frameworks have a long-term utility in face analysis, taking into account the anticipated improvements in data collection, data storage, processing speeds, and application scenarios expected as the discipline develops further. The book covers face acquisition through 3D scanners and 3D face pre-processing, before examining the three main approaches for 3D facial surface analysis and recognition: facial curves; facial surface features; and 3D morphable models. Whilst the focus of these chapters is fundamentals and methodologies, the algorithms provided are tested on facial biometric data, thereby continually showing how the methods can be applied. Key features: • Explores the underlying mathematics and will apply these mathematical techniques to 3D face analysis and recognition • Provides coverage of a wide range of applications including biometrics, forensic applications, facial expression analysis, and model fitting to 2D images • Contains numerous exercises and algorithms throughout the book
Facial Expression Analysis With Graphical Models
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Author : Lifeng Shang
language : en
Publisher: Open Dissertation Press
Release Date : 2017-01-26
Facial Expression Analysis With Graphical Models written by Lifeng Shang and has been published by Open Dissertation Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-01-26 with categories.
This dissertation, "Facial Expression Analysis With Graphical Models" by Lifeng, Shang, 尚利峰, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Facial expression recognition has become an active research topic in recent years due to its applications in human computer interfaces and data-driven animation. In this thesis, we focus on the problem of how to e?ectively use domain, temporal and categorical information of facial expressions to help computer understand human emotions. Over the past decades, many techniques (such as neural networks, Gaussian processes, support vector machines, etc.) have been applied to facial expression analysis. Recently graphical models have emerged as a general framework for applying probabilistic models. They provide a natural framework for describing the generative process of facial expressions. However, these models often su?er from too many latent variables or too complex model structures, which makes learning and inference dicult. In this thesis, we will try to analyze the deformation of facial expression by introducing some recently developed graphical models (e.g. latent topic model) or improving the recognition ability of some already widely used models (e.g. HMM). In this thesis, we develop three di?erent graphical models with di?erent representational assumptions: categories being represented by prototypes, sets of exemplars and topics in between. Our rst model incorporates exemplar-based representation into graphical models. To further improve computational e- ciency of the proposed model, we build it in a local linear subspace constructed by principal component analysis. The second model is an extension of the recently developed topic model by introducing temporal and categorical information into Latent Dirichlet Allocation model. In our discriminative temporal topic model (DTTM), temporal information is integrated by placing an asymmetric Dirichlet prior over document-topic distributions. The discriminative ability is improved by a supervised term weighting scheme. We describe the resulting DTTM in detail and show how it can be applied to facial expression recognition. Our third model is a nonparametric discriminative variation of HMM. HMM can be viewed as a prototype model, and transition parameters act as the prototype for one category. To increase the discrimination ability of HMM at both class level and state level, we introduce linear interpolation with maximum entropy (LIME) and member- ship coecients to HMM. Furthermore, we present a general formula for output probability estimation, which provides a way to develop new HMM. Experimental results show that the performance of some existing HMMs can be improved by integrating the proposed nonparametric kernel method and parameters adaption formula. In conclusion, this thesis develops three di?erent graphical models by (i) combining exemplar-based model with graphical models, (ii) introducing temporal and categorical information into Latent Dirichlet Allocation (LDA) topic model, and (iii) increasing the discrimination ability of HMM at both hidden state level and class level. DOI: 10.5353/th_b4784948 Subjects: Face perception - Data processing Image processing - Digital techniques Human-computer interaction
Advances In Data Driven Computing And Intelligent Systems
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Author : Swagatam Das
language : en
Publisher: Springer Nature
Release Date :
Advances In Data Driven Computing And Intelligent Systems written by Swagatam Das and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on with categories.
Handbook Of Face Recognition
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Author : Stan Z. Li
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-12-06
Handbook Of Face Recognition written by Stan Z. Li and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-06 with Computers categories.
Although the history of computer-aided face recognition stretches back to the 1960s, automatic face recognition remains an unsolved problem and still offers a great challenge to computer-vision and pattern recognition researchers. This handbook is a comprehensive account of face recognition research and technology, written by a group of leading international researchers. Twelve chapters cover all the sub-areas and major components for designing operational face recognition systems. Background, modern techniques, recent results, and challenges and future directions are considered. The book is aimed at practitioners and professionals planning to work in face recognition or wanting to become familiar with the state-of- the-art technology. A comprehensive handbook, by leading research authorities, on the concepts, methods, and algorithms for automated face detection and recognition. Essential reference resource for researchers and professionals in biometric security, computer vision, and video image analysis.
Handbook Of Face Recognition
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Author : Stan Z. Li
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-08-22
Handbook Of Face Recognition written by Stan Z. Li and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-08-22 with Computers categories.
This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational face recognition systems. After a thorough introductory chapter, each of the following chapters focus on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions. Features: fully updated, revised and expanded, covering the entire spectrum of concepts, methods, and algorithms for automated face detection and recognition systems; provides comprehensive coverage of face detection, tracking, alignment, feature extraction, and recognition technologies, and issues in evaluation, systems, security, and applications; contains numerous step-by-step algorithms; describes a broad range of applications; presents contributions from an international selection of experts; integrates numerous supporting graphs, tables, charts, and performance data.
Facial Expression Recognition And Computing An Interdisciplinary Perspective
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Author : Ke Zhao
language : en
Publisher: Frontiers Media SA
Release Date : 2022-06-23
Facial Expression Recognition And Computing An Interdisciplinary Perspective written by Ke Zhao and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-23 with Science categories.
Learning Techniques For Multi Modal Facial Analysis
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Author : Munawar Hayat
language : en
Publisher:
Release Date : 2015
Learning Techniques For Multi Modal Facial Analysis written by Munawar Hayat and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.
Face and facial expression recognition are two important facial analysis tasks with numerous real life applications. This dissertation investigates the suitability of different data modalities for these two tasks. Specifically, the dissertation first proposes a method for the automatic analysis of textured 3D videos for facial expression recognition. The task of face recognition is then considered across multiple data modalities which include 3D static images and videos, RGB-D images acquired from low cost Kinect sensor and low quality grey scale images acquired from surveillance cameras. The dissertation is organized as a set of papers already published or submitted to journals or internationally refereed conferences. The dissertation first evaluates and compares existing methods of spatiotemporal feature description for 2D video-based facial expression recognition. It then presents an automatic framework, which exploits the dynamics of textured 3D videos for the recognizing six discrete facial expressions. Specifically, local video-patches of variable lengths are extracted from numerous locations of the training videos and represented as points on the Grassmannian manifold. An efficient graph-based spectral clustering algorithm is proposed to separately cluster these points for every expression class. Using a valid Grassmannian kernel function, the resulting cluster centers are embedded into a Reproducing Kernel Hilbert Space (RKHS) where six binary SVM models are learnt for classification. The dissertation then proposes manifold learning, deep learning and discriminative learning techniques for face recognition across multiple data modalities. First, a computationally efficient low level feature description method is proposed for face recognition from 3D static images. A method for the spatiotemporal evaluation of 3D videos is then presented. Face recognition from RGB-D images acquired from Kinect sensor is then considered as an image set classification problem. A method for the compact description of image sets using Riemannian geometry is proposed in this regards. For classification, SVM models are learnt on the Lie group of Riemannian manifold. The dissertation then finally considers face recognition from low quality imagery acquired from easily installable video surveillance cameras. Face recognition from this data modality is also studied under the framework of image set classification. For this purpose, two independent high performing methods are proposed. The first method learns deep reconstruction models, which can automatically discover the underlying complex geometric structure of the images in an image set. The second method empowers well developed binary classifiers for the task of multi-class image set classification. Compared to the existing binary to multiclass extension strategies, the proposed method is very efficient since it only trains few binary classifiers and uses very few images for the training of each of these classifiers.
