Stephan Tschechne Chair for Image Understanding Computer Science Technische Universität München Designing vs. Learning the Objective Function for Face Model Fitting Abschlußvortrag Diplomarbeit

/15 Technische Universität München Stephan Tschechne Model-based Image Understanding Face Model Fitting Objective Functions Experimental Results Overview:

/15 Technische Universität München Stephan Tschechne Understanding Facial Images  Various Applications  Identification  Mimics  Hands-free Control  Image Database: 850 Natural Images

/15 Technische Universität München Stephan 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)

/15 Technische Universität München Stephan 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)=0.3F(Img,p3)=0.6

/15 Technische Universität München Stephan Tschechne Requirements Formulation of Requirements for Robust Objective Functions: R1: Correct Position of Minimum R2: One Minimum R3: Continuous Behaviour R4: Gradient Vectors Point Away Optimal Objective Functions:

/15 Technische Universität München Stephan Tschechne Traditional Objective Functions Calculation of Objective Function Value ? Intuitive approach: Manual Selection of Salient Features: Distance to Edges..

/15 Technische Universität München Stephan Tschechne Traditional Objective Functions …or Distance to Edges from Skin Colour Images

/15 Technische Universität München Stephan Tschechne Traditional Approach  Problem: Desired Edges are not the Strongest Ones

/15 Technische Universität München Stephan Tschechne Contribution  Robust Objective Function  Better Fulfillment of the Requirements ?!

/15 Technische Universität München Stephan 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

/15 Technische Universität München Stephan Tschechne  Training Data: Feature Values F i  Result R  Deliberately Move Instance to Gather Values  Model Trees Learn: F(Feature Values)  Result F 1 =134 F 2 =66 …  R=0.7 F 1 =54 F 2 =234 …  R=0.0 F 1 =281 F 2 =11 …  R=0.5

/15 Technische Universität München Stephan Tschechne Experimental Results Center: Correct Parameters Axes: Variation of p towards …...translation..deformation

/15 Technische Universität München Stephan 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

/15 Technische Universität München Stephan Tschechne The End. Any Questions ?