Research Article
BibTex RIS Cite

DESTEK VEKTÖR MAKİNELERİ KULLANARAK YÜZ TANIMA UYGULAMASI GELİŞTİRİLMESİ

Year 2018, Volume: 13 Issue: 2, 119 - 127, 21.04.2018

Abstract

Yüz tanıma sistemleri günümüzde hızlı büyüyen
ve geniş bir uygulama alanına sahip biyometrik teknolojilerden biridir. Destek
Vektör Makineleri, istatistiksel öğrenme algoritmasına göre çalışan,
sınıflandırma ve regresyon problemlerinin çözümünde kullanılan bir makine
öğrenme algoritmasıdır. Bu çalışmada Destek Vektör Makineleri kullanarak yüz
tanıma uygulaması gerçekleştirilmiştir. Destek Vektör Makinelerinde
sınıflandırma işlemi için radyal tabanlı çekirdek fonksiyonu tercih
edilmiştir.  Destek Vektör Makineleri
tarafından sınıflandırılacak görüntülerin tespit edilebilmesi için ön işleme ve
öznitelik çıkarımı işlemleri uygulanmıştır. Görüntüler üzerindeki yüz bölgeleri
kırpılmış ve 20x20 piksel olarak yeniden boyutlandırılmıştır. Kırpılan
görüntüler üzerindeki özyüzler Temel Bileşen Analizi kullanılarak bulunmuştur. Temel
Bileşen Analizi, veri setindeki güçlü özellikleri ortaya çıkarmak için kullanılan
bir yöntemdir. Uygulama OpenCV kullanarak Visual Studio 2013 ortamında
gerçekleştirilmiştir. Test verilerinin başarım oranı %80 olarak elde
edilmiştir.

References

  • [1] Parmar, D.N. and Mehta, B.B., (2013). Face Recognition Methods & Applications. Int. J. Computer Technology & Applications, Volume:4, Number:1, pp:84-86.
  • [2] Tuzcuoğlu, H., (2003). Yapay Zeka Teknikleri, Depremde Kullanılması ve Küme Kuramları. DEÜ Mühendislik Fakültesi Fen ve Mühendislik Dergisi, Cilt:5, Number:1, ss:73-88.
  • [3] Tikoo, S. and Malik, N., (2016). Detection Segmentation and Recognition of Face and Its Features Using Neural Network. Journal of Biosensors & Bioelectronics, Volume:7, Number:2, ss:1-5.
  • [4] Marqués, I., (2010). Face Recogenition Algorithms. Biskay: Universidad del Pais Vasco.
  • [5] Turk, M. and Pentland, A., (1991). Eigenfaces for Recognition. Journal of Cognitive Neuroscience, Volume:3, Number:1, pp:71-86.
  • [6] Leung, T., Burl, M., and Perona, P., (1995). Finding faces in Clutttered Scenes Using Labeled Random Graph Matching. pp:637-644, International Conference Computer Vision, Cambridge, USA.
  • [7] Lawrence, S., Giles, C.L., Tsoi, A.C., and Back, A.D., (1997). Face Recognition: A Convolutional Neural-Network Approach. IEEE Trans Neural Network, Volume:8, Number:1, pp:98-113.
  • [8] Etemad, K. and Chellappa, R., (1997). Discrimant Analysis for Recognition of Human Face Images. J. Opt. Soc. Am. A, Volume:14, Number:8, pp:1724-1733.
  • [9] Rowley, H., Baluja, S. and Kanade, T., (1998). Neural Network Based Face Detection. IEEE Trans. Pattern Analysis and Machine Intelligence, Volume:20 Number:1 pp:23-38.
  • [10] Schneiderman, H. and Kanade, T., (2000). A Statistical Method for 3d Object Detection Applied to Faces and Cars. pp:746-751, IEEE Conf. Computer Vision and Pattern Recognition, Güney Carolina, USA, 2000.
  • [11] Yuen, P.C. and Lai, J.H., (2002). Face Representation Using İndependent Component Analysis. Pattern Recognition, Volume:35, Number:6, pp:1247-1257.
  • [12] Yang, M.H., Kriegmn, D.J., and Ahuja, N., (2002). Detecting Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume:24, Number:1, pp:34-36.
  • [13] Vapnik, V.N. and Lerner, A.Y., (1963). Recognition of Patterns With Help of Generalized Portraits. Translated from Avtamarika I Telernckllanjka, Volume:24, Number:6, pp:774-780.
  • [14] Vapnik, V.N., (1982). Realism and Instrumentalism: Classical Statics and VC Theory (1960-1980). pp:411-423, Springer Science & Business Media, New York, USA.
  • [15] Boser, B.E., Guyon, I.M., and Vapnik, V.N., (1992). A Training Algorithm for Optimal Margin Classifiers. Proc. 5th ACM Workshop on Computational Learning Theory (COLT). Pittsburgh, Pennsylvania, USA, pp:144-152.
  • [16] Cortes, C. and Vapnik, V., (1995). Support Vector Networks. Machine Learning, Volume:20, Number:1, pp:273-297.
  • [17] Çatalbaş, M.C., (2014). Temel Bileşenler Analizi ve Kanonik Korelasyon Analizi İle İmge Tanıma ve Sınıflandırma. Yayınlanmış Yüksek Lisans Tezi. Ankara: Hacettepe Üniversitesi Fen Bilimleri Enstitüsü.
  • [18] Pearson, K., (1901). On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine, Volume:2, Number:6, pp:559-572.
  • [19] Tayyar, N. ve Tekin, S., (2013). İMKB-100 Endeksinin Destek Vektör Makineleri İle Günlük, Haftalık ve Aylık Veriler Kullanarak Tahmin Edilmesi. AİBÜ Sosyal Bilimler Enstitüsü Dergisi, Cilt:13, Sayı:1, ss:189-217.
  • [20] Osuna, E.E., Freund, R., and Girosi, F., (1997). Support Vector Machines: Training and Applications. Center for biological and computational learning department brain and cognitive sciences, Neural Networks for Signal Processing,
  • [21] Gunn, S.R., (1998). Support Vector Machines for Classification And Regression. United Kingtom: Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science.
  • [22] Yang, J., Zhang, D., Member, S., Frangi, A.F., and Yang, J.Y., (2004). Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume:26, Number:1, pp:131-137.
  • [23] Pearson, K., (1901). On Lines And Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine, Volume:2, Number:1, pp:559-572.
  • [24] Hotelling, H., (1933). Analysis of a Complex of Statistical Variables into Principal Components. Journal of Educational Psychology, Volume:24, Number:6, pp:417-441.
  • [25] Sivorich, L. and Kirby, M., (1986). Low-Dimensional Procedure for the Characterization of Human Faces. Journal of the Optical Society of America, Volume:4. Number:3, pp:520-524.
  • [26] Gharamaleki, P.S. and Seyedarabi, H., (2015). Face Recognition Using Eigenfaces, PCA and Support Vector Machines. European Journal of Applied Engineering and Scientific Research, Volume:4, Number:3, pp:24-30.
Year 2018, Volume: 13 Issue: 2, 119 - 127, 21.04.2018

Abstract


References

  • [1] Parmar, D.N. and Mehta, B.B., (2013). Face Recognition Methods & Applications. Int. J. Computer Technology & Applications, Volume:4, Number:1, pp:84-86.
  • [2] Tuzcuoğlu, H., (2003). Yapay Zeka Teknikleri, Depremde Kullanılması ve Küme Kuramları. DEÜ Mühendislik Fakültesi Fen ve Mühendislik Dergisi, Cilt:5, Number:1, ss:73-88.
  • [3] Tikoo, S. and Malik, N., (2016). Detection Segmentation and Recognition of Face and Its Features Using Neural Network. Journal of Biosensors & Bioelectronics, Volume:7, Number:2, ss:1-5.
  • [4] Marqués, I., (2010). Face Recogenition Algorithms. Biskay: Universidad del Pais Vasco.
  • [5] Turk, M. and Pentland, A., (1991). Eigenfaces for Recognition. Journal of Cognitive Neuroscience, Volume:3, Number:1, pp:71-86.
  • [6] Leung, T., Burl, M., and Perona, P., (1995). Finding faces in Clutttered Scenes Using Labeled Random Graph Matching. pp:637-644, International Conference Computer Vision, Cambridge, USA.
  • [7] Lawrence, S., Giles, C.L., Tsoi, A.C., and Back, A.D., (1997). Face Recognition: A Convolutional Neural-Network Approach. IEEE Trans Neural Network, Volume:8, Number:1, pp:98-113.
  • [8] Etemad, K. and Chellappa, R., (1997). Discrimant Analysis for Recognition of Human Face Images. J. Opt. Soc. Am. A, Volume:14, Number:8, pp:1724-1733.
  • [9] Rowley, H., Baluja, S. and Kanade, T., (1998). Neural Network Based Face Detection. IEEE Trans. Pattern Analysis and Machine Intelligence, Volume:20 Number:1 pp:23-38.
  • [10] Schneiderman, H. and Kanade, T., (2000). A Statistical Method for 3d Object Detection Applied to Faces and Cars. pp:746-751, IEEE Conf. Computer Vision and Pattern Recognition, Güney Carolina, USA, 2000.
  • [11] Yuen, P.C. and Lai, J.H., (2002). Face Representation Using İndependent Component Analysis. Pattern Recognition, Volume:35, Number:6, pp:1247-1257.
  • [12] Yang, M.H., Kriegmn, D.J., and Ahuja, N., (2002). Detecting Faces in Images: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume:24, Number:1, pp:34-36.
  • [13] Vapnik, V.N. and Lerner, A.Y., (1963). Recognition of Patterns With Help of Generalized Portraits. Translated from Avtamarika I Telernckllanjka, Volume:24, Number:6, pp:774-780.
  • [14] Vapnik, V.N., (1982). Realism and Instrumentalism: Classical Statics and VC Theory (1960-1980). pp:411-423, Springer Science & Business Media, New York, USA.
  • [15] Boser, B.E., Guyon, I.M., and Vapnik, V.N., (1992). A Training Algorithm for Optimal Margin Classifiers. Proc. 5th ACM Workshop on Computational Learning Theory (COLT). Pittsburgh, Pennsylvania, USA, pp:144-152.
  • [16] Cortes, C. and Vapnik, V., (1995). Support Vector Networks. Machine Learning, Volume:20, Number:1, pp:273-297.
  • [17] Çatalbaş, M.C., (2014). Temel Bileşenler Analizi ve Kanonik Korelasyon Analizi İle İmge Tanıma ve Sınıflandırma. Yayınlanmış Yüksek Lisans Tezi. Ankara: Hacettepe Üniversitesi Fen Bilimleri Enstitüsü.
  • [18] Pearson, K., (1901). On Lines and Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine, Volume:2, Number:6, pp:559-572.
  • [19] Tayyar, N. ve Tekin, S., (2013). İMKB-100 Endeksinin Destek Vektör Makineleri İle Günlük, Haftalık ve Aylık Veriler Kullanarak Tahmin Edilmesi. AİBÜ Sosyal Bilimler Enstitüsü Dergisi, Cilt:13, Sayı:1, ss:189-217.
  • [20] Osuna, E.E., Freund, R., and Girosi, F., (1997). Support Vector Machines: Training and Applications. Center for biological and computational learning department brain and cognitive sciences, Neural Networks for Signal Processing,
  • [21] Gunn, S.R., (1998). Support Vector Machines for Classification And Regression. United Kingtom: Faculty of Engineering, Science and Mathematics School of Electronics and Computer Science.
  • [22] Yang, J., Zhang, D., Member, S., Frangi, A.F., and Yang, J.Y., (2004). Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume:26, Number:1, pp:131-137.
  • [23] Pearson, K., (1901). On Lines And Planes of Closest Fit to Systems of Points in Space. Philosophical Magazine, Volume:2, Number:1, pp:559-572.
  • [24] Hotelling, H., (1933). Analysis of a Complex of Statistical Variables into Principal Components. Journal of Educational Psychology, Volume:24, Number:6, pp:417-441.
  • [25] Sivorich, L. and Kirby, M., (1986). Low-Dimensional Procedure for the Characterization of Human Faces. Journal of the Optical Society of America, Volume:4. Number:3, pp:520-524.
  • [26] Gharamaleki, P.S. and Seyedarabi, H., (2015). Face Recognition Using Eigenfaces, PCA and Support Vector Machines. European Journal of Applied Engineering and Scientific Research, Volume:4, Number:3, pp:24-30.
There are 26 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Nesrin Aydın Atasoy

Derya Tabak

Publication Date April 21, 2018
Published in Issue Year 2018 Volume: 13 Issue: 2

Cite

APA Aydın Atasoy, N., & Tabak, D. (2018). DESTEK VEKTÖR MAKİNELERİ KULLANARAK YÜZ TANIMA UYGULAMASI GELİŞTİRİLMESİ. Engineering Sciences, 13(2), 119-127.
AMA Aydın Atasoy N, Tabak D. DESTEK VEKTÖR MAKİNELERİ KULLANARAK YÜZ TANIMA UYGULAMASI GELİŞTİRİLMESİ. Engineering Sciences. April 2018;13(2):119-127.
Chicago Aydın Atasoy, Nesrin, and Derya Tabak. “DESTEK VEKTÖR MAKİNELERİ KULLANARAK YÜZ TANIMA UYGULAMASI GELİŞTİRİLMESİ”. Engineering Sciences 13, no. 2 (April 2018): 119-27.
EndNote Aydın Atasoy N, Tabak D (April 1, 2018) DESTEK VEKTÖR MAKİNELERİ KULLANARAK YÜZ TANIMA UYGULAMASI GELİŞTİRİLMESİ. Engineering Sciences 13 2 119–127.
IEEE N. Aydın Atasoy and D. Tabak, “DESTEK VEKTÖR MAKİNELERİ KULLANARAK YÜZ TANIMA UYGULAMASI GELİŞTİRİLMESİ”, Engineering Sciences, vol. 13, no. 2, pp. 119–127, 2018.
ISNAD Aydın Atasoy, Nesrin - Tabak, Derya. “DESTEK VEKTÖR MAKİNELERİ KULLANARAK YÜZ TANIMA UYGULAMASI GELİŞTİRİLMESİ”. Engineering Sciences 13/2 (April 2018), 119-127.
JAMA Aydın Atasoy N, Tabak D. DESTEK VEKTÖR MAKİNELERİ KULLANARAK YÜZ TANIMA UYGULAMASI GELİŞTİRİLMESİ. Engineering Sciences. 2018;13:119–127.
MLA Aydın Atasoy, Nesrin and Derya Tabak. “DESTEK VEKTÖR MAKİNELERİ KULLANARAK YÜZ TANIMA UYGULAMASI GELİŞTİRİLMESİ”. Engineering Sciences, vol. 13, no. 2, 2018, pp. 119-27.
Vancouver Aydın Atasoy N, Tabak D. DESTEK VEKTÖR MAKİNELERİ KULLANARAK YÜZ TANIMA UYGULAMASI GELİŞTİRİLMESİ. Engineering Sciences. 2018;13(2):119-27.