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|Title:||Face Recognition using the most Representative Sift Images||Authors:||Dagher, Issam
Sallak, Nour El
|Affiliations:||Department of Computer Engineering||Keywords:||Face recognition
|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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