Open AccessUsing Local Features in Face Recognition Systems
Author(s) -
Yıldız Aydın,
Funda Akar
Publication year - 2018
Publication title -
akıllı sistemler ve uygulamaları dergisi
Language(s) - English
Resource type - Journals
ISSN - 2667-6893
DOI - 10.54856/jiswa.201812041
Subject(s) - artificial intelligence , scale invariant feature transform , pattern recognition (psychology) , computer science , classifier (uml) , feature extraction , facial recognition system , affine transformation , feature selection , support vector machine , three dimensional face recognition , computer vision , feature (linguistics) , face (sociological concept) , face detection , mathematics , social science , linguistics , philosophy , sociology , pure mathematics
Among the many applications in the field of computer vision, face recognition systems; is a subject that has been studied extensively and has been working for a long time. In general, the success of facial recognition systems, which consist of feature extraction and classifier steps, depends not only on the classifier but also on the features used. In a face recognition system, the feature selection is to obtain distinctive features for recognition of different facial images of interest. For this purpose, SIFT, SURF and SIFT + SURF features, which are unchanging features to scaling and affine transformations, are used in this study. In addition, to be able to compare with these local features, the HOG feature which is a global feature, also has been added to the study. Classification was performed using support vector machine. Experimental results show that local features are more successful than the global feature HOG.