1. Large-Scale, Real-World Face Recognition in Movie Trailers Week 2-3 Alan Wright (Facial Recog. pictures taken from Enrique Gortez)

2. Preliminary Steps Extract Facial Tracks - Working on MATLAB code now Worked on detecting blurry images, no solid results. Extract the features from the facial tracks. Build framework to load and test data. Begin with baseline testing (Sparse, min, meant, etc) Algorithm development…

3. Blur Detection Canny Edge Detection Hough transform Hough Lines Find perpendicular line Using that perpendicular line, get two parallel lines on each side of the Hough line. Choose 10 points on each side to find the gradient.

4. Hough Lines

5. Using Perpendicular lines

6. Gradient Points

7. Good Edge Mean Intensity Pixels 1 - 20 (10 on each side of the Hough Line)

8. Bad Edge

9. Results Bad Hough Lines Dataset is not ideal for this algorithm, but works well on larger photos.

10. Facial Recognition

11. Linear Combination + x 2 + x 3 + x 4 + x 5 + x 6 + x 7 + x 8 + x 9 Test Image = x1x1 Training Images

12. Linear Combination y Testing A = = Training x Coefficients

13. Now in videos… We have: Instead of:

14. Baseline

15. Sparse Representation-based Classification (SRC) + x 2 + x 3 + x 4 + x 5 + x 6 + x 7 + x 8 + x 9 Test Image = x1x1 Training Images + 0

16. SRC Sparse Linear

17. SRC Method – Impose sparsity on coefficient vector We want to minimize the coefficient sum to enforce sparsity. (Wright09) Minimize coef.

18. SRC Classification – Compute Per Class Residual Error where j is a given class (or person)

19. Possible Baseline Algorithms Sum up the coefficient vector and take: average, min, etc.. SRC linear combination. Then creating our own algorithm…

20. Related Papers Read “Face Tracking and Recognition with Visual Constraints in Real-World Videos” “Face Tracking and Recognition with Visual Constraints in Real-World Videos” – Project Page Project Page “Large Scale Learning and Recognition of Faces in Web Videos” “Large Scale Learning and Recognition of Faces in Web Videos”