Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/1989
Title: Face Recognition using the most Representative Sift Images
Authors: Dagher, Issam 
Sallak, Nour El
Hazim, Hani
Affiliations: Department of Computer Engineering 
Keywords: Face recognition
SIFT
LDP
Clustering
Matching
Issue Date: 2014
Part of: International journal of signal processing image processing and pattern recognition
Volume: 7
Issue: 1
Start page: 225
End page: 236
Abstract: 
In this paper, face recognition using the most representative SIFT images is presented. It is based on obtaining the SIFT (SCALE INVARIANT FEATURE TRANSFORM) features in different regions of each training image. Those regions were obtained using the K-means clustering algorithm applied on the key-points obtained from the SIFT algorithm. Based on these features, an algorithm which will get the most representative images of each face is presented. In the test phase, an unknown face image is recognized according to those representative images. In order to show its effectiveness this algorithm is compared to other SIFT algorithms and to the LDP algorithm for different databases.
URI: https://scholarhub.balamand.edu.lb/handle/uob/1989
Open URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Computer Engineering

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