Face Recognition Using Dictionary Learning Algorithms

Simple item page
dc.contributor.authorKhalili, Mohammad Mehdi
dc.contributor.supervisorGulliver, T. Aaron
dc.date.accessioned2019-05-10T19:28:54Z
dc.date.available2019-05-10T19:28:54Z
dc.date.copyright2019en_US
dc.date.issued2019-05-10
dc.degree.departmentDepartment of Electrical and Computer Engineering
dc.degree.levelMaster of Engineering M.Eng.en_US
dc.description.abstractFace recognition is one of the most challenging and important topics in computer vision, pattern recognition and image processing. It has experienced a recent advance by using dictionary learning algorithms. These algorithms benefit from sparse coding techniques to achieve more accurate and faster classifications. Three dictionary learning algorithms for face recognition, Label Consistent K-SVD (LC-KSVD), Fisher Discriminative Dictionary Learning (FDDL), and Support Vector Guided Dictionary Learning (SVGDL), are investigated in this project. The reason for choosing these algorithms is their high accuracy in dictionary learning based image recognition. Accuracy, speed, and variability are used as measures to test these algorithms. The number of training images, atoms, and iterations are considered as parameters in order to evaluate the algorithms. The extended Yale B image database is used for testing. Simulations are performed using MATLAB. The results obtained indicate that SVGDL is the best algorithm followed by LC-KSVD and then FDDL.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/10872
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectFace Recognitionen_US
dc.subjectDictionary Learning Algorithmsen_US
dc.subjectImage Processingen_US
dc.titleFace Recognition Using Dictionary Learning Algorithmsen_US
dc.typeprojecten_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Khalili_Mohammad Mehdi_MEng_2019.pdf.pdf
Size:
2.46 MB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Master's Projects
 
University of Victoria Libraries

We acknowledge and respect the Lək̓ʷəŋən (Songhees and Esquimalt) Peoples on whose territory the university stands, and the Lək̓ʷəŋən and W̱SÁNEĆ Peoples whose historical relationships with the land continue to this day.