Analysis and Modeling of Faces and Gestures

Third International Workshop, AMFG 2007 Rio de Janeiro, Brazil, October 20, 2007 Proceedings
2007
ISBN: 978-3-540-75689-7
Verlag: Springer Berlin Heidelberg

Buch, Englisch, Band 4778, 304 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 487 g

Reihe: Lecture Notes in Computer Science

The 2007 IEEE International Workshop on Analysis and Modeling of Faces and Gestures(AMFG)isthethirdworkshopofitstypeorganizedinconjunctionwith ICCV, this time in Rio de Janeiro, Brazil. Our primary goal is to bring together researchersand research groupsto review the status of recognition, analysis and modeling of face, gesture, activity, and behavior; to discuss the challenges that we are facing; and to explore future directions. This year we received 55 submissions. Each paper was reviewed by three program committee members. The whole reviewing process was double blind. However,due to size limit, we were only able to accommodate 22 papers, among which 8 are orals and 14 are posters. The topics covered by these accepted papers include feature representation, 3D face, robust recognition under pose and illumination variations,video-basedface recognition,learning,facial motion analysis, body pose estimation, and sign recognition. A special word of thanks goes to Dr. Feng Zhao, our organizing chair, for his dedication and great e?orts in maintaining both the online submission system and workshop website and in handling most of the author contacts. We are indebted to the advisory committee members for their valuable suggestions and to the program committee members for their hard work and timely reviews. Finally, we thank Cognitec System GmbH and Siemens Corporate Research for their sponsorship. October 2007 S. Kevin Zhou Wen-Yi Zhao Xiaoou Tang Shaogang Gong Organization AMFG 2007 was held in conjunction with ICCV 2007. Workshop Chairs S. Kevin Zhou Siemens Corporate Research Wen-Yi Zhao Intuitive Surgical, Inc.

Zielgruppe

Research

Autoren/Hrsg.

Zhou, S. Kevin

Gong, Shaogang

Zhao, Wen-Yi

Tang, Xiaoou

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Spiele-Programmierung, Rendering, Animation
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmierung: Methoden und Allgemeines
• Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Wissensbasierte Systeme, Expertensysteme
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Grafikprogrammierung
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik

Inhaltsverzeichnis

Oral - I.- Learning Personal Specific Facial Dynamics for Face Recognition from Videos.- A New Probabilistic Model for Recognizing Signs with Systematic Modulations.- Model-Based Stereo with Occlusions.- View Invariant Head Recognition by Hybrid PCA Based Reconstruction.- Poster - I.- Person-Independent Monocular Tracking of Face and Facial Actions with Multilinear Models.- Automatic Facial Expression Recognition Using Boosted Discriminatory Classifiers.- Generating Body Surface Deformation Using Level Set Method.- Patch-Based Pose Inference with a Mixture of Density Estimators.- Integrating Multiple Visual Cues for Robust Real-Time 3D Face Tracking.- Model-Assisted 3D Face Reconstruction from Video.- Human Perambulation as a Self Calibrating Biometric.- Oral - II.- Detecting, Localizing and Classifying Visual Traits from Arbitrary Viewpoints Using Probabilistic Local Feature Modeling.- Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions.- Structured Ordinal Features for Appearance-Based Object Representation.- SODA-Boosting and Its Application to Gender Recognition.- Poster - II.- Single Image Subspace for Face Recognition.- Human Face Processing with 1.5D Models.- Fusing Gabor and LBP Feature Sets for Kernel-Based Face Recognition.- A Unified Framework of Subspace and Distance Metric Learning for Face Recognition.- Face Recognition Based on Pose-Variant Image Synthesis and Multi-level Multi-feature Fusion.- Towards Pose-Invariant 2D Face Classification for Surveillance.- Robust Face Recognition Strategies Using Feed-Forward Architectures and Parts.