The Performance of LBP and NSVC Combination Applied to Face Classification

The growing demand in the field of security led to the development of interesting approaches in face classification. These works are interested since their beginning in extracting the invariant features of the face to build a single model easily identifiable by classification algorithms. Our goal in...

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Bibliographic Details
Published in:Applied computational intelligence and soft computing 2016-01, Vol.2016 (2016), p.1-10
Main Authors: Hachimi, Hanaa, Nassih, Bouchra, Amine, Aouatif, Ngadi, Mohammed, El-Attar, Adnane
Format: Article
Language:English
Subjects:
Access control
Accuracy
Algorithms
Classification
Classifiers
Computer information security
Face recognition
Feature extraction
Image detection
Invariants
Methods
Neighborhoods
Soft computing
Wavelet transforms
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Summary:The growing demand in the field of security led to the development of interesting approaches in face classification. These works are interested since their beginning in extracting the invariant features of the face to build a single model easily identifiable by classification algorithms. Our goal in this article is to develop more efficient practical methods for face detection. We present a new fast and accurate approach based on local binary patterns (LBP) for the extraction of the features that is combined with the new classifier Neighboring Support Vector Classifier (NSVC) for classification. The experimental results on different natural images show that the proposed method can get very good results at a very short detection time. The best precision obtained by LBP-NSVC exceeds 99%.
ISSN:1687-9724
1687-9732
DOI:10.1155/2016/8272796