stephan tschechne chair for image understanding computer science technische universität münchen...
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/15 Technische Universität München Stephan Tschechne Understanding Facial Images Various Applications Identification Mimics Hands-free Control Image Database: 850 Natural ImagesTRANSCRIPT
Stephan Tschechne
Chair for Image UnderstandingComputer Science
Technische Universität München
Designing vs. Learning the Objective Function for Face Model Fitting
Abschlußvortrag Diplomarbeit
28.6.06 2/15Technische Universität MünchenStephan Tschechne
• Model-based Image Understanding
• Face Model Fitting
• Objective Functions
• Experimental Results
Overview:
28.6.06 3/15Technische Universität MünchenStephan Tschechne
Understanding Facial Images Various Applications
Identification Mimics Hands-free Control
Image Database: 850 Natural Images
28.6.06 4/15Technische Universität MünchenStephan Tschechne
Deformable Face Model 134 Contour Points Perform PCA Point Distribution Model
Description of an Instance: Parameter Vector p = (x,y,scaling,rotation,deform1..deform17)
28.6.06 5/15Technische Universität MünchenStephan Tschechne
Objective Function• Fitting Algorithms Search for Correct p:
Optimisation Problem• Objective Function Calculates Fitting Accuracy • Lowest Value for Correct Solution
F(Img,p1)=0.0 F(Img,p2)=0.3 F(Img,p3)=0.6
28.6.06 6/15Technische Universität MünchenStephan Tschechne
RequirementsFormulation of Requirements for Robust Objective Functions:
R1: Correct Position of MinimumR2: One MinimumR3: Continuous BehaviourR4: Gradient Vectors Point Away
Optimal Objective Functions:
28.6.06 7/15Technische Universität MünchenStephan Tschechne
Traditional Objective FunctionsCalculation of Objective Function Value ?• Intuitive approach:
Manual Selection of Salient Features:• Distance to Edges..
28.6.06 8/15Technische Universität MünchenStephan Tschechne
Traditional Objective Functions
• …or Distance to Edges from Skin Colour Images
28.6.06 9/15Technische Universität MünchenStephan Tschechne
Traditional Approach Problem: Desired Edges are not the Strongest Ones
28.6.06 10/15Technische Universität MünchenStephan Tschechne
Contribution
Robust Objective Function Better Fulfillment of the Requirements
?!
28.6.06 11/15Technische Universität MünchenStephan Tschechne
Learning the Robust Objective Function Training data:
Ground Truth from Image Database Haar-like Features Desired Value from
Optimal Objective Function
Machine Learns Rules with Model Trees
28.6.06 12/15Technische Universität MünchenStephan Tschechne
Training Data: Feature Values Fi Result R Deliberately Move Instance to Gather Values
Model Trees Learn: F(Feature Values) Result
F1=134F2=66 … R=0.7
F1=54F2=234 … R=0.0
F1=281F2=11 … R=0.5
28.6.06 13/15Technische Universität MünchenStephan Tschechne
Experimental ResultsCenter: Correct ParametersAxes: Variation of p towards ….
..translation ..deformation
28.6.06 14/15Technische Universität MünchenStephan Tschechne
Challenges:
Image Database with Natural Images Database High Dimensionality of Parameter Vector Verification of Requirements
Future research: Model Tracking Other Models: 3D, Appearance Models.. Different Features Other Positions for Features
28.6.06 15/15Technische Universität MünchenStephan Tschechne
The End.
Any Questions ?