1 / 27

Fast Face Detection Sami Romdhani Phil Torr Bernhard Sch ölkopf Andrew Blake Mike Tipping

Fast Face Detection Sami Romdhani Phil Torr Bernhard Sch ölkopf Andrew Blake Mike Tipping. Menu. Previous Work Support Vector Machine Sequential Evaluation Incremental Training Results Conclusion. 1. Classification Machine. Face Non-face. 2. Search.  825,880 patches

signe-parker
Télécharger la présentation

Fast Face Detection Sami Romdhani Phil Torr Bernhard Sch ölkopf Andrew Blake Mike Tipping

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. Fast Face Detection Sami Romdhani Phil Torr Bernhard Schölkopf Andrew Blake Mike Tipping

  2. Menu Previous Work Support Vector Machine Sequential Evaluation Incremental Training Results Conclusion

  3. 1. Classification Machine Face Non-face 2. Search  825,880 patches Computationally intensive Rowley Face detection = localising faces in images is possible, but slow

  4. Improving Speed : Rowley’s way 20 Instead of : 20 30 Learn on : 30 Rowley’s Detection rate decreases to 75%, speed : 5 to 7 s.

  5. Improving Speed : our way Idea : most of the patches can be easily discriminated For these, classification must be fast Hence, classification complexity must be variable : classifier = set of cheap filters of increasing complexity

  6. Support Vector Machines(Vapnik, 1995) Training SVM Training Support Vectors : …

  7. Support Vector Machines(Vapnik, 1995) 2. Classification Is this path a face ? … D D D D D D D D D > T  Face <= T Non-Face Output

  8. Reduced Set Vectors : Reduced Set Vector Post-Processing with by an iterative procedure Find which minimise Find which minimise …(Schölkopf et al. 1999):

  9. Sequential Evaluation < 0 classified as a non-face >= 0 continue   < 0 classified as a non-face >= 0 continue …  < 0 classified as a non-face >= 0 use the full SVM < 0 classified as a non-face >= 0 classified as a face Is patch a face ?

  10. Sequential Evaluation Example: Original SVM 0 % training error, 31 Support Vectors

  11. Sequential Evaluation Example 41.7 % training error, 1 Reduced Vectors

  12. Sequential Evaluation Example 36.7 % training error, 2 Reduced Vectors

  13. Sequential Evaluation Example 21.7 % training error, 3 Reduced Vectors

  14. Sequential Evaluation Example 5 % training error, 4 Reduced Vectors

  15. Sequential Evaluation Example 0 % training error, 9 Reduced Vectors

  16. Sequential Evaluation Example 0 % training error, 13 Reduced Vectors

  17. Rejection Example F1 : 3.7% F10 : 0.72% f20 : 0.003% f30 : 0.00005% 312x400 image, 7 subsampling level, 10.4 s. Average number of filters per patch : 1.51

  18. 1280x1024 image, 11 subsampling levels, 80s Average number of filter per patch : 6.7 First filter : 19.8 % patches remaining

  19. 1280x1024 image, 11 subsampling levels, 80s Average number of filter per patch : 6.7 Filter 10 : 0.74 % patches remaining

  20. 1280x1024 image, 11 subsampling levels, 80s Average number of filter per patch : 6.7 Filter 20 : 0.06 % patches remaining

  21. 1280x1024 image, 11 subsampling levels, 80s Average number of filter per patch : 6.7 Filter 30 : 0.01 % patches remaining

  22. 1280x1024 image, 11 subsampling levels, 80s Average number of filter per patch : 6.7 Filter 70 : 0.007 % patches remaining

  23. Incremental Training Original Training Set SVM Training Detection with very low thresholds New Images Detected Patches Support Vectors

  24. Pre-Processing We shift pre-processing to training time, instead of detection time (Rowley et al. 1998)

  25. Results

  26. Future Work • Investigate fast preprocessing at detection time • Change the Reduced Set Vector algorithm so that it takes the data into account :Now : Future : • Change the kernel so that it takes info about face variation into account :Now : Future : • Try Tipping’s Relevance VM instead of Reduced VM • Colour • Once a face is detected, use that prior information • Recode by a good SDE

  27. Conclusion • New Fast Face Detection algorithm : • Based on a early rejection classification • Speed dependent on the complexity of the data • Accuracy-wise, not yet on a par with state of the art, but promising enough

More Related