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.

Inhalt

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.