UID:
almahu_9949972448302882
Format:
XI, 424 p.
,
online resource.
Edition:
1st ed. 2005.
ISBN:
9783540320746
Series Statement:
Image Processing, Computer Vision, Pattern Recognition, and Graphics, 3723
Content:
During the last 30 years, face recognition and related problems such as face detection/tracking and facial expression recognition have attracted researchers from both the engineering and psychology communities. In addition, extensive research has been carried out to study hand and body gestures. The understanding of how humans perceive these important cues has significant scientific value and extensive applications. For example, human-computer interaction, visual surveillance, and smart video indexing are active application areas. Aiming towards putting such amazing perception capability onto computer systems, researchers have made substantial progress. However, technological challenges still exist in many aspects. Following a format similar to the IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG) 2003, this one-day workshop (AMFG 2005) provided a focused international forum to bring together well-known researchers and research groups to review the status of recognition, analysis and modeling of faces and gestures, to discuss the challenges that we are facing, and to explore future directions. Overall, 30 papers were selected from 90 submitted manuscripts. The topics of these papers range from feature representation, robust recognition, learning, and 3D modeling to psychology. In addition, two invited talks were given, by Prof. Kanade and Dr. Phillips. The technical program was organized into four oral sessions and two poster sessions. This workshop would not have been possible without the timely reviews provided by the members of the Technical Program Committee under a tight schedule. October 2005 Wenyi Zhao Shaogang Gong Xiaoou Tang.
Note:
Oral Sessions -- Facial Expression Analysis -- Modeling Micro-patterns for Feature Extraction -- Facial Expression Analysis Using Nonlinear Decomposable Generative Models -- Kernel Correlation Filter Based Redundant Class-Dependence Feature Analysis (KCFA) on FRGC2.0 Data -- Learning to Fuse 3D+2D Based Face Recognition at Both Feature and Decision Levels -- A New Combinatorial Approach to Supervised Learning: Application to Gait Recognition -- Learning a Dynamic Classification Method to Detect Faces and Identify Facial Expression -- How to Train a Classifier Based on the Huge Face Database? -- Non-rigid Face Modelling Using Shape Priors -- Parametric Stereo for Multi-pose Face Recognition and 3D-Face Modeling -- An Investigation of Model Bias in 3D Face Tracking -- Poster Sessions -- Facial Expression Representation Based on Timing Structures in Faces -- A Practical Face Relighting Method for Directional Lighting Normalization -- Face Recognition Based on Local Steerable Feature and Random Subspace LDA -- Online Feature Selection Using Mutual Information for Real-Time Multi-view Object Tracking -- A Binary Decision Tree Implementation of a Boosted Strong Classifier -- Robust Facial Landmark Detection for Intelligent Vehicle System -- Pose-Encoded Spherical Harmonics for Robust Face Recognition Using a Single Image -- Advantages of 3D Methods for Face Recognition Research in Humans -- The CMU Face In Action (FIA) Database -- Robust Automatic Human Identification Using Face, Mouth, and Acoustic Information -- AdaBoost Gabor Fisher Classifier for Face Recognition -- Automatic 3D Facial Expression Analysis in Videos -- Real-Time Modeling of Face Deformation for 3D Head Pose Estimation -- An Integrated Two-Stage Framework for Robust Head Pose Estimation -- Gabor-Eigen-Whiten-Cosine: A Robust Scheme for Face Recognition -- Two-Dimensional Non-negative Matrix Factorization for Face Representation and Recognition -- Face View Synthesis Across Large Angles -- Regularization of LDA for Face Recognition: A Post-processing Approach -- Linear Programming for Matching in Human Body Gesture Recognition -- Combination of Projectional and Locational Decompositions for Robust Face Recognition.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783540292296
Additional Edition:
Printed edition: ISBN 9783540816546
Language:
English
URL:
https://doi.org/10.1007/11564386
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