《人脸识别课件英文.pptVIP

Topic 5. Human Faces Face Image Databases Face Image Databases References. Outline Face Detection Methods [5] Face vs non-face Clsutering Distance Measure Deformable Face Template Local Deformation and Global Transform Deformable Model of Facial Features Upper Face Action Units Lower Face Action Units Templates for Various States Templates for Various States Features for Action Unit Recognition Classification from Feature Vector Recognition Rate Apparence Model: Landmarks on a face Eigen-vectors for Geometry and Photometry Apparence Model Face Localization and Recognition A Linear HMM Model for Face Face Detection Sample of the 4D space Multi-scale Detection Edge Features Decision Tree Examples of Decision Trees Bounds Analysis Some Examples Face Prior Learning: Experimental Details 83 key points defined on face 720 individuals with all kinds of types Dimension reduced to 33 by PCA 40000 samples drawn by the inhomogeneous Gibbs sampler in each Monte Carlo integration 50 features pursuit Total runtime: about 5 days on a PIII 667, 256MB PC Obs Syn Samples (1) Synthesis Samples Synthesis Samples 50 Observed Histograms 50 Synthesized Histograms Evaluation only. Created with Aspose.Slides for .NET 3.5 Client Profile 5.2.0.0. Copyright 2004-2011 Aspose Pty Ltd. Observed faces Synthesized faces without any features Evaluation only. Created with Aspose.Slides for .NET 3.5 Client Profile 5.2.0.0. Copyright 2004-2011 Aspose Pty Ltd. Synthesized faces with 20 features Synthesized faces with 10 features Evaluation only. Created with Aspose.Slides for .NET 3.5 Client Profile 5.2.0.0. Copyright 2004-2011 Aspose Pty Ltd. Synthesized faces with 30 features Synthesized faces with 50 features Evaluation only. Created with Aspose.Slides for .NET 3.5 Client Profile 5.2.0.0. Copyright 2004-2011 Aspose Pty Ltd. Human face is extensively studied in vision. Depending on the applications, there are a long list of tasks [5]: Detection and Recognition: Face detection