Week 5 Report

1. Week 5 Report Shelby Thompson

2. This week… • 1 research paper • Coded more • Used PubFig83 dataset to run tests on

3. Using the PubFig83 dataset… • Used training, testing and image IDs • Compute pairwise distance between images • Did so by computing cosine distance between matrix rows

4. Plotted version of transposed matrix…

5. Using PubFig83 dataset… • Next created a zero matrix as big as distance matrix • Iterated over the training and testing IDs • If the train and test IDs were equal set that spot in the zero matrix to 1

6. Plotted zero matrix (final)…

7. Created kNN graph… • Iterated over the distance matrix • Sorted it in declining order • Found top 5 k-neighboors • Set all other rows in the matrix to 0

8. Created kNN graph (cont)… • Iterated over each row/threshold • Iterate based on (i,i) where I is row number • Set all rows above threshold to 0 or Inf (they appear different)

9. Graphing kNN (using 0)…

10. Graphed kNN (using Inf)…

11. Research Paper A Survey of Recent Advances in Face Detection By: Cha Zhang and Zhengyou Zhang (Briefly reviewed this article)

12. Article Focus… • Review past decade of development in face detection • Uses Viola-Jones face detector • Survey other algorithms and feature extractors • Goal is to find the best algorithm

13. Experiment… • The authors focused more on surveying and researching current face detection methods • Examined different face detection methods and how they improved • Focus on boosted algorithms, like AdaBoost • Examined popular programs like iPhoto and Picassa

14. Goals for next week… • Continue to work on my codes • Experiment with writing some of my MatLab code in Java • Find out what Enrique wants me to do next

15. End of Week 5