1. Robust 3D Head Pose Classification using Wavelets by Mukesh C. Motwani Dr. Frederick C. Harris, Jr., Thesis Advisor December 5 th, 2002 A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Computer Engineering University of Nevada, Reno

2. Overview Problem Description Problem Description Proposed Solution Proposed Solution Wavelets and PCA Wavelets and PCA Future Work Future Work Questions and Answers Questions and Answers Demo Demo

3. What is Pose Classification ?

4. Potential Applications Face Recognition Face Recognition

5. Potential Applications Virtual Reality Virtual Reality

6. Potential Applications Video Conferencing Video Conferencing

7. Potential Applications Robotics Robotics

8. Potential Applications Driver Vigilance Driver Vigilance

9. What makes Pose Detection so difficult? Identity Identity

10. What makes Pose Detection so difficult? Face Detection Face Detection

11. What makes Pose Detection so difficult? Facial Expression Facial Expression

12. What makes Pose Detection so difficult? Changes in distance from Camera Changes in distance from Camera

13. What makes Pose Detection so difficult? Occlusion Occlusion

14. What makes Pose Detection so difficult? Changes in Illumination Changes in Illumination

15. Previous Work Feature Based Feature Based Appearance Based Appearance Based Model Based Model Based Other Combined Approaches Other Combined Approaches

16. Feature Based Pose Detection Kalman Filter Kalman Filter Gabor Jets Gabor Jets Elastic Graph Bunching Elastic Graph Bunching

17. Appearance Based Pose Detection Template Matching Template Matching Neural Networks / Wavelet Neural Networks Neural Networks / Wavelet Neural Networks SVM SVM PCA PCA

18. Model Based Pose Detection Skin Color Skin Color Anthropometry Anthropometry

19. Other Combined Approaches Volumetric Frequency Representation Volumetric Frequency Representation Hairline contour Hairline contour Optical Flow Optical Flow

20. PCA Based Approach “View Based” “View Based” “Parametric” “Parametric” 1. Motivation for choosing PCA 2. Sub categories in PCA based approach

21. Block Diagram Training Stage Training Stage Training Images Detection Stage Detection Stage Detected Pose Detected PoseQueryImage Discrete Wavelet Transform Principal Component Analysis Manifold Plot Discrete Wavelet Transform Principal Component Analysis Eigen Space Projection

22. Wavelet Transform Filter Banks Filter Banks

23. Wavelet Transform Subband Decomposition Subband Decomposition Low Pass Output High Pass Output LLHL LHHH

24. DWT of Lena Image

25. Selection of Resolution Level Distribution of energy in LL subband at different levels of discrete wavelet transform

26. Selection of Resolution Level

27. Principal component analysis (PCA) 1.Mean Image (Enlarged) 2. Compute covariance matrix 3. Compute eigen-vectors and eigen-coefficients

28. Manifold Plot

29. Experimentation Facial Expression Facial Expression

30. Experimentation Changes in distance from camera Changes in distance from camera

31. Experimentation Changes in Illumination Changes in Illumination

32. Experimentation Identity change Identity change

33. Performance Classification accuracy of 84% Classification accuracy of 84% Real time capability 12-15 fps Real time capability 12-15 fps

34. Conclusion Faster Faster More Robust More Robust Cheaper Cheaper

35. Future Work Face Detection Face Detection Identity Independent Identity Independent Undecimated Wavelet Transform Undecimated Wavelet Transform

36. Questions ? / Answers Questions ? / Answers Real-time Demo Real-time Demo