Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/3360
Title: Face recognition using the SIFT most representative images
Authors: Sallak, Nour El
Hazim, Hani
Advisors: Dagher, Issam 
Subjects: Human face recognition (Computer science)
Issue Date: 2013
Abstract: 
In this project, face recognition using the most SIFT representative images is presented. It is based on obtaining the SIFT features in different regions of each training image using the K-means algorithm. Based on these features, the most representative images of each face are obtained. A test image is recognized according to those representative images. The matching technique used is presented. This algorithm is compared to other algorithms for different database.
Description: 
Includes bibliographical references (p.27-28).

Supervised by Dr. Issam Dagher.
URI: https://scholarhub.balamand.edu.lb/handle/uob/3360
Rights: This object is protected by copyright, and is made available here for research and educational purposes. Permission to reuse, publish, or reproduce the object beyond the personal and educational use exceptions must be obtained from the copyright holder
Ezproxy URL: Link to full text
Type: Project
Appears in Collections:UOB Theses and Projects

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