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4EyesFace-Realtime face detection, tracking, alignment and recognition

4EyesFace-Realtime face detection, tracking, alignment and recognition. Changbo Hu, Rogerio Feris and Matthew Turk. Overview. Introduction Face Detection and Pose tracking Face Alignment Face Recognition Conclusions. Introduction. Detection. Pose tracking. Alignment. Recognition.

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4EyesFace-Realtime face detection, tracking, alignment and recognition

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  1. 4EyesFace-Realtime face detection, tracking, alignment and recognition Changbo Hu, Rogerio Feris and Matthew Turk

  2. Overview • Introduction • Face Detection and Pose tracking • Face Alignment • Face Recognition • Conclusions

  3. Introduction Detection Pose tracking Alignment Recognition

  4. Introduction • Why this is a difficult problem? • Facial Expressions, Illumination Changes, Pose, etc. • Object • Develop a fully automatic system, suitable for real-time applications to locate and track human faces, then to align and recognize the face. • Evaluate it on a large dataset.

  5. Face Detection [Viola and Jones, 2001] • Simple features, which can be computed very fast. • A variant of Adaboost is used both to select the features and to train the classifier. • Classifiers are combined in a “cascade” which allows background regions of the image to be quickly discarded.

  6. Face detection

  7. Pose tracking Based on Kentaro Toyama’s IFA framework

  8. Face Alignment • Active Appearance Model (AAM) Statistical Shape Model (PCA) Statistical Texture Model (PCA)

  9. Problem: Partial Occlusion Active Wavelet Networks (AWN) (on BMVC’03) Main idea: Replace AAM texture model by a wavelet network Face alignment

  10. Face Alignment Similar performance to AAM in images under normal conditions. More robust against partial occlusions.

  11. Face Alignment Using 9 wavelets, the system requires only 3 ms per iteration. In general, at most 10 iterations are sufficiently for good convergence (PIV 1.6Ghz).

  12. Multi-View Face Alignment • View selection by pose tracker

  13. Multi-View Face Alignment

  14. Face recognition • online recognition • HMM based face recognition

  15. Face recognition • Large dataset evaluation • FERET DataSet • 1196 different individuals • With ground truth of eye corners

  16. Face recognition

  17. Face recognition

  18. Face Recognition

  19. Face Recognition

  20. Conclusion • We develop a system to do human face detection, tracking, alignment and recognition • In this system, we invented new methods AWN and extent to multi-view AWN • We implement the related detection and pose tracking • Evaluate our method on large dataset

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