Blanz And Vetter Program

Curriculum Vitae Sami Romdhani Email [email protected] Date of birth 30 September 1972. Of the European student exchange program ’ERASMUS’. MSc thesis: ’Face Recognition. Blanz, and T. Face identification by fitting a 3D morphable model using linear shape.

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This is an implementation of real-time inverse compositional Active Appearance Models (AAMs), as described in the 'Active Appearance Models Revisited' paper by Iain Matthews and Simon Baker. Experimental support is provided for the 3D extensions to inverse compositional AAMs, also developed by Matthews and Baker in their '2D vs.

Vetter

3D Deformable Face Models' and 'Real-Time Combined 2D+3D Active Appearance Models' papers. As an aid to ease the task of building the 3D shape model needed by the 3D extensions, an implementation of the closed-form shape-from-motion algorithm by Jing Xiao and Takeo Kanade is provided. And while I can't guarantee the quality of the results given by this latter implementation, it shouldn't be far from correct as it's heavily based on Xiao's implementation.

Look up 'icaam' on Sourceforge.net to get the latest release, the data required by the examples and more! What version are you using? Make sure you're using the latest version.

Also please tell me if you're using Windows or Linux Matlab (no one ever really tested this under Linux). To understand how to use the 3D algorithms, look at the tesitrain.m and tesisfm.m examples. The motion dataset has some low quality data (the files with the exclamation marks), and I don't remember if those should be used or not. Finally, I never really got fantastic results with that algorithm and that data. Your best shot is to either run the shape-from-motion a lot of time (as it's randomized) or to use your own 2D motion data.

Thankyou for the project. I was able to train my data with the help of examples in the project. But i am having problem with the fitting algorithm fit3d. The function either gives an error or gives an output with high error(the model does not fit on the face) when i provide my own pic. I have tried 'tesi/motion' to make the shape model as well. Still i get incorrect results. When i give (dataset/IMM) to make the shape model.

I get an error 'Number of landmarks in initshape and/or number of colors in imagedata are not consistent with the model.'