Abstract
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.
Publisher
Islerya Medikal ve Bilisim Teknolojileri
Reference20 articles.
1. Kolap AD, Shrikhande SV, Jagtap NK. Review on various face recognition techniques. International Journal of Innovative Research in Computer and Communication Engineering 2015; 3(3): 2398-2404.
2. Liau HF, Ang LM, Seng KP. A multiview face recognition system based on eigenface method. In 2008 International Symposium on Information Technology, August 26-28, 2008, Kuala Lumpur, Malaysia, pp. 1-5.
3. Satone M, Kharate GK. Selection of eigenvectors for face recognition. International Journal of Advanced Computer Science and Applications (IJACSA) 2013; 4(3).
4. Dagher I, Nachar R. Face recognition using IPCA-ICA algorithm. IEEE Transactions On Pattern Analysis and Machine Intelligence 2006; 28(6): 996-1000.
5. Eleyan A, Demirel H. Co-occurrence based statistical approach for face recognition. In 2009 24th International Symposium on Computer and Information Sciences, September 14-16, 2009, Guzelyurt, Northern Cyprus, pp. 611-615.
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献