KaydedilenlerDerginizi ekleyin

Dergiler / Electronic Letters on Science and Engineering / 2020 / Volume: 16 Issue: 2

Face Recognition using Local Binary Patterns

Özlem Orhan(BANDIRMA ONYEDİ EYLÜL ÜNİVERSİTESİ)0000-0003-0058-0431
Muhammed Milani(BANDIRMA ONYEDI EYLUL UNIVERSITY)0000-0002-5295-4215
Bahar Milani(BANDIRMA ONYEDI EYLUL UNIVERSITY)0000-0003-2450-0280
Sayfa
—
Yayın Tarihi
2020-12-30
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The results analyzed in this method show its efficiency.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Face recognition is one of the topics from which it is difficult to prepare a complete computational model.
📊
Temel Bulgular & Veri:Unlike other detection's these are a high-level task, and a variety of methods have been proposed.
💡
Sonuç & Katkı:The results analyzed in this method show its efficiency.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Face recognition · LBP · Detection

Abstract

Face recognition is one of the topics from which it is difficult to prepare a complete computational model. Unlike other detection's these are a high-level task, and a variety of methods have been proposed. On the other hand, face recognition is needed as a suitable data source for various applications that lead to the recognition of individuals. In this article, we will examine this issue and consider a method based on Local Binary Patterns and provide algorithms for it. The results analyzed in this method show its efficiency.

🔖

Bu Makaleye Atıf Yap

Orhan, Ö., Milani, M., Milani, B. (2020). Face Recognition using Local Binary Patterns. Electronic Letters on Science and Engineering, Volume: 16 Issue: 2. https://ajindex.com/articles/1181008

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (15)

  1. Hjelmås, E., & Low, B. K. (2001). Face detection: A survey. Computer vision and image understanding, 83(3), 236-274.
  2. Yang, M. H., Kriegman, D. J., & Ahuja, N. (2002). Detecting faces in images: A survey. IEEE Transactions on pattern analysis and machine intelligence, 24(1), 34-58.
  3. Rakshit, P., Basu, R., Paul, S., Bhattacharyya, S., Mistri, J., & Nath, I. (2020). Face Detection using Support Vector Mechine with PCA. Available at SSRN 3515989.
  4. Viola, P., Jones, M. J., & Snow, D. (2005). Detecting pedestrians using patterns of motion and appearance. International Journal of Computer Vision, 63(2), 153-161.
  5. Goldstein, A. J., Harmon, L. D., & Lesk, A. B. (1971). Identification of human faces. Proceedings of the IEEE, 59(5), 748-760.
  6. Solanki, K., & Pittalia, P. (2016). Review of face recognition techniques. International Journal of Computer Applications, 133(12), 20-24.
  7. Sirovich, L., & Kirby, M. (1987). Low-dimensional procedure for the characterization of human faces. Josa a, 4(3), 519-524
  8. Kim, T. K., & Kittler, J. (2005). Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image. IEEE transactions on pattern analysis and machine intelligence, 27(3), 318-327.
  9. Ahonen, T., Hadid, A., & Pietikainen, M. (2006). Face description with local binary patterns: Application to face recognition. IEEE transactions on pattern analysis and machine intelligence, 28(12), 2037-2041 [ Meng, J., Gao, Y., Wang, X., Lin, T., & Zhang, J. (2010, August). Face recognition based on local binary patterns with threshold. In 2010 IEEE International Conference on Granular Computing (pp. 352-356). IEEE.
  10. Nagpal, S., Singh, M., Singh, R., & Vatsa, M. (2019). Deep learning for face recognition: Pride or prejudiced?. arXiv preprint arXiv:1904.01219.
  11. Prasad, P. S., Pathak, R., Gunjan, V. K., & Rao, H. R. (2020). Deep learning based representation for face recognition. In ICCCE 2019 (pp. 419-424). Springer, Singapore.
  12. Mandal, T., Majumdar, A., & Wu, Q. J. (2007, August). Face recognition by curvelet based feature extraction. In International Conference Image Analysis and Recognition (pp. 806-817). Springer, Berlin, Heidelberg.
  13. Ramesha, K. B. R. K., Raja, K. B., Venugopal, K. R., & Patnaik, L. M. (2010). Feature extraction-based face recognition, gender and age classification.
  14. Wei, J., Jian-Qi, Z., & Xiang, Z. (2011). Face recognition method based on support vector machine and particle swarm optimization. Expert Systems with Applications, 38(4), 4390-4393.
  15. Guo, G., & Zhang, N. (2019). A survey on deep learning based face recognition. Computer Vision and Image Understanding, 189, 102805.