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

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Presentation on theme: "Fast Face Detection Sami Romdhani Phil Torr Bernhard Schölkopf Andrew Blake Mike Tipping."— 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 2. Search Face detection = localising faces in images is possible, but slow Rowley Classification Machine Face Non-face ,880 patches Computationally intensive

4 Improving Speed : Rowleys way Instead of : Learn on : Rowleys 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) Support Vectors : SVM Training … Training

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

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

9 Sequential Evaluation Is patch a face ? < 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

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 : % 312x400 image, 7 subsampling level, 10.4 s. Average number of filters per patch : 1.51

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

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

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

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

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

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

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

25 Results Detection rateFalse Positive Rowley 1 - best detection91.7 % % Rowley 2 - lowest FP77.9 %2.4*10 -6 % MSR Cam 1 - best detect.80.6 % % MSR Cam 2 - lowest FP57.8 % %

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 Tippings 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


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