Jaringan Syaraf Tiruan Perambatan Balik Untuk Pengenalan Wajah

*Nahdi Saubari -  Universitas Diponegoro, Indonesia
Rizal Isnanto -  Universitas Diponegoro
Kusworo Adi -  Universitas Diponegoro
Received: 3 Apr 2016; Published: 30 Nov 2016.
Open Access
Citation Format:
Article Info
Section: Research Articles
Language: EN
Full Text:
Statistics: 1066 2016
Abstract
This research discusses about face detection and face recognition in an image. Face detection has only two classifications, i.e face and not face. Face recognition is compatible with some classifications of a number individuals who want to be recognized. Face detection and face recognition in thi study using Haar-Like Feature method and Artificial Neural Network Backpropagation. A method Haar-Like Feature used for detection and extraction in an image, because the clasification on this method showed success at used to detect image of the face. Artificial Neural Network Backpropagation is a training algorithm that is used to do training simulated on facial image data training stored in a database. This study uses Ms. Excel 2007 as database with 10 individual sample image, every image in each individuals having three distance with every range has four defferent light intensities, so that the data training stored in the database reached 120 data training. The results shows that the face detection and face recognition which is developed can recognize a face image with an average accuracy rate reaches 80,8% for each distance.
Keywords
Detection, Recognition, Haar-Like Feature, ANN Backpropagation

Article Metrics:

  1. Alasdair. M., 2004. An Introduction to Digital Image Processing with Matlab. Notes for SCM2511 Image Processing 1, School of Computer Science and Mathematics Victoria University of Technology.
  2. Agustin. M., 2012. Penggunaan jaringan syaraf tiruan backpropagation untuk seleksi penerimaan mahasiswa baru pada jurusan teknik komputer di Politeknik Negeri Sriwijaya. Tesis, Universitas Diponegoro, Semarang.
  3. Antara, I.P.R, Sumarminingsih. E, Handoyo. S., 2010. Model jaringan syaraf tiruan backrpopagation dengan input berdasarkan model regresi terbaik. Tesis, Universitas Brawijaya, Malang.
  4. Levi. Ki, Weiss. Y., 2004. Learning object detection from a small number of examples: the importance of good features, Proceedings of CVPR 2004 IEEE International Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 53–60.
  5. Li. S.Z, Zhang. Z., 2004. Floatboost learning and statistical face detection, IEEE Trans. Pattern Anal. Mach. Intell. 26 (9) 1112–1123.
  6. Lienhart. R, Maydt, J., 2002. An extended set of Haar-like features for rapid object detection, Proceedings of ICIP 2002 IEEE International onference on Image Processing, vol. 1, pp. 900–903.
  7. Matondang. Z.A., 2013. Jaringan Syaraf Tiruan Dengan Algoritma Backpropagation Unuk Penentuan Kelulusaan Sidang Skripsi, ISSN 2301-9425.
  8. Mita. T, Kaneko. T., 2005. O. Hori, Joint Haar-like features for face detection, Proceedings of ICCV 2005 10th IEEE International Conference on Computer Vision, vol. 2, pp. 1619–1626.
  9. McAndrew Alasdair., 2004. An Introduction to Digital Image Processing with Matlab, Notes for SCM2511 Image Processing 1, School of Computer Science and Mathematics Victoria University of Technology.
  10. Pambudi. W.S, Simorangkir. B.M.N., 2012. Facetracker Menggunakan MetodeHaar like feature dan PID Model Simulasi, Jurnal Teknologi dan Informatika (TEKNOMATIKA).
  11. Pan. H, Zhu. Y, Xia. L., 2013. Efficient and accurate face detection using heterogeneous feature descriptors and feature selection, International Journal of Computer Vision and Image Understanding, 117, 12-28.
  12. Pham. M.T, Cham. T.J., 2007. Fast training and selection and Haar features using statistics in boosting-based face detection, Proceedings of ICCV 2007 11th IEEE International Conference on Computer Vision, pp. 1–7.
  13. Sutojo. T, Mulyanto. E, Suharto. V., 2010. Kecerdasan Buatan, Andi, Yogyakarta.
  14. Te-Hsiu. S and Fang-Chin. T., 2008. Using backpropagation neural network for face recognation with 2D + 3D hybrid information, Expert System with Application, 35, 361-372.
  15. Viola, Paul and Jones. J., 2001. Rapid object detection using boosted cascade of simple features, Proceedings IEEE Conf. on Computer Vision and Pattern Recognition.
  16. Zhang. J, Ji. N, liu. J, Pan. J, Meng. D., 2015. Enhancing performance of the backpropagation algorithm via sparse response regularization, International Journal of Neurocomputing, 153, 20-40.