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Presenter: Mohammad Mehdi Khalili
Supervisor:

Date: Thu, April 18, 2019
Time: 13:00:00 - 14:00:00
Place: EOW 430

ABSTRACT

Face 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 stability 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.