Open AccessA Novel Feature Extraction Descriptor for Face Recognition
Author(s) -
Ahmed B Salem Salamh,
Halil İbrahim Akyüz
Publication year - 2022
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.4624
Subject(s) - artificial intelligence , pattern recognition (psychology) , computer science , local binary patterns , face (sociological concept) , pairwise comparison , facial recognition system , similarity (geometry) , feature extraction , feature (linguistics) , support vector machine , image (mathematics) , computer vision , histogram , social science , linguistics , philosophy , sociology
This paper presents a new feature extraction technique for face recognition. The new model, called multi-descriptor, is based on the well-known method of local binary patterns. It involves many different neighborhoods of the central pixel. Its unique advantage is that this descriptor allows the use of different neighborhood sizes instead of only one point. This structure ensures reasonable effectiveness and also provides the possibility to obtain a different distribution of features. Based on the new descriptor, a face recognition model using the pairwise feature descriptor based on the proposed descriptor was developed in this work, and local binary patterns were created to investigate the similarity and dissimilarity between the two models. For both models, the training was done using the support vector machine method on different face databases to overcome face recognition problems such as camera distance, expression, large head size, and illumination variations. The proposed technique achieved perfect accuracy on almost all tested databases including the Extended Yale B and Grimace database.