1 / 15

University of São Paulo Institute of Mathematics and Statistics Department of Computer Science

University of São Paulo Institute of Mathematics and Statistics Department of Computer Science Tracking Facial Features Using Gabor Wavelet Networks. Rogério Schmidt Feris Roberto Marcondes Cesar Junior {rferis,cesar}@ime.usp.br. Outline . Motivation Wavelet Transforms

yestin
Download Presentation

University of São Paulo Institute of Mathematics and Statistics Department of Computer Science

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. University of São Paulo Institute of Mathematics and Statistics Department of Computer Science Tracking Facial Features Using Gabor Wavelet Networks Rogério Schmidt Feris Roberto Marcondes Cesar Junior {rferis,cesar}@ime.usp.br

  2. Outline • Motivation • Wavelet Transforms • Face Representation Using GWNs • Facial Feature Tracking • Experimental Results • Future Work

  3. Motivation • Face Recognition from Video Sequences • Problem Assumptions

  4. Wavelet Transforms • Continuous Wavelet Transform • Discrete Wavelet Transform • Wavelet Networks

  5. Face Representation Using GWNs • Mother Wavelet: Odd-Gabor Function

  6. Face Representation Using GWNs

  7. Face Representation Using GWNs Original Image Wavelet Representation Wavelet parameters are chosen from the continuous parameters space !!

  8. Face Representation Using GWNs • Progressive Attention Interest 32, 52, 100 and 320 wavelets • Direct Calculation of Weights

  9. Facial Feature Tracking • Repositioning

  10. Facial Feature Tracking • Superwavelet • Reparametrization

  11. Facial Feature Tracking • Initialization • Repositioning in each Frame • Facial Feature Tracking

  12. Experimental Results • Efficiency and Robustness http://www.ime.usp.br/~rferis

  13. Future Work • Tracking Based on Wavelet Weights • Face Detection Using GWNs • 3D Wavelet Model

  14. Questions ? Rogério Schmidt Feris rferis@ime.usp.br http://www.ime.usp.br/~rferis

  15. Experimental Results • Efficiency and Robustness http://www.ime.usp.br/~rferis

More Related