The Performance of LBP and NSVC Combination Applied to Face Classification - Mohammed Ngadi; Aouatif Amine; Bouchra Nassih; Hanaa Hachimi; Adnane El-Attar (Applied Computational Intelligence and Soft Computing)
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%.