Read the following paper for details of the algorithm - Robust Face Recognition via Sparse Representation by John Wright, Arvind Ganesh, and Yi Ma , Coordinated Science Laboratory, University of Illinois at Urbana-Champaign and Allen Yang, Electrical Engineering and Computer Science, University of California Berkeley. The database used is MIT-CBCL and YaleB database which was got from http://cbcl.mit.edu/software-datasets/heisele/facerecognition-database.html and http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html.
hello sir i am a M-tech student and i have taken up this paper as my project.i have seen your code and is running good.i have a question regarding the code whether it is same as the Donoho concept of compressed sensing and if it is so i want to know how your code calculates the sparse coefficients.
The low success rate is because I am doing no Preprocessing for orientation and illumination correction for this database. If you implement a good preprocessing algorithm the success rate should increase. And having used the L2 norm as a classifier for an earlier project using MIT-CBCL database personally I felt the L1 norm gives a better performance (73% to 90%).
Success rate in percentage is-(for Yale dataset)
It is too low. Is the problem the choosing of "L1 Norm Minimization"?You choose '\'.
thank you very match
Added the databases to the package
Can u please give us the database
Can u please give us the database?
I have added the databases to the zipped package along with the code. Only the paths have to be updated and this code should work.
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