While I've had this blog a bit abandonned since the break, the research has continued during this 6 weeks of the semester. Here's a list of new features the code has achieved:

  • Ive changed the classification algorithm from KNN to SVM. Now i train a single SVM for each class, using all the faces in the database that dont belong to the class as negative examples. 
  • All the intermediate results are saved (as csv) this way, when data that is needed has been calculated previosuly, it can be reused. This has also been done thinking on  the smartphone implementation, where we can transfer just the proyection matrix and avoid long calculations
  • ive started using the Yale database of faces to get a standarized result that can be compared with other algorithms. (150 faces, 15 classes)
  • the code has been streamlined, removing and changing some opertaions to make it faster and easier to interpret
  • I also made a testing code to analyse the optimal number of eigenvectors to use, it runs the code with different number of eigenvectors and outputs save files with results for later analysis
at this point, the SVM has something missing, i thin the interpretation of the results is failing, as before i got some accuracy of 60% while now i always get 6.6%. I'm currently working on solving this.

Ive also started writing (using LaTeX), with the document strcuture finished and all the requiered packages and files created, the content seems to be progressing.

This following week i will use for three things, implement a python algorithm to test my algoritm against, fix the SVM and continue the writing.


Post a Comment