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