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Face and Pose Tracking

Face and Pose Tracking. Kat Bradley Kaylin Spitz. General Layout (Detection). Left Image. Right Image. Face Detection. Face Location (Left). Feature Correspondence. Feature Location (Left). Feature Detection. 3D Points. Pose Detection. General Layout (Tracking). Previous

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Face and Pose Tracking

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  1. Face and Pose Tracking Kat Bradley Kaylin Spitz

  2. General Layout (Detection) Left Image Right Image Face Detection Face Location (Left) Feature Correspondence Feature Location (Left) Feature Detection 3D Points Pose Detection

  3. General Layout (Tracking) Previous Features Left Image Right Image Previous Face Face Tracking Feature Tracking Face Location (Left) Feature Location (Left) Feature Correspondence Feature Location (Left) Feature Filtering 3D Points If unsuccessful Pose Detection Re-Detection

  4. Face Detection • Performed on left frame initially & every 20 frames • Uses Haar classifiers • Slow (~350 ms for one frame)

  5. Face Detection Performance • Requires frontal face • Occasionally (about 5%) misidentifies • Possible Improvements

  6. Feature Detection • SURF features • Detects in a single frame • Takes about 130 ms

  7. Face Tracking: Original • Mean-shift color procedure proposed by Comaniciu. et al • Target: histogram of color distribution from initial frame • Tracking by comparing distribution to target distribution (mean-shift)

  8. Face Tracking: Improvements • Using two color spaces (robustness) • Sampling (speed) • Takes about 40 ms per frame

  9. Face Tracking Performance • Highly dependent on initial face • Robust to changes in size and expression changes • Issues with lighting changes

  10. Feature Tracking • Optical Flow (Lucas-Kanade) • Performed in left frame

  11. Feature Filtering • Finds mean and standard deviation of offset (for points in face) • Filters away points many standard deviations away from mean • Filters away points far from face • Signals if few points are in the face (to trigger re-detection)

  12. Feature Correspondence • Optical Flow (Lucas-Kanade) • Gives matched points for pose detection • Filters out points with high error

  13. Optical Flow Performances • Both moderately reliable without filters • Without filters, problems with occlusions • With filter, highly reliable

  14. Pose Approximation • Fits a plane to 3D points • Normal of plane = approximate direction of face

  15. Pose Performance • Best on well-distributed features • Issues with poorly-distributed features • Possible improvements

  16. Efficiency/Speed Tested on 800x640 image with face about 100x100.

  17. Acknowledgements Thanks to: Ruzena Bacjsy, Gregorij Kurillo, and everybody at the Tele-Immersive Lab for help, support, and lots of explaining. Distributed Research Experiences for Undergraduates for funding.

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