Face Detection in Image Frames and Matching Through Face Database

Yıl 2019, Cilt: 7 Sayı: 2, 478 - 493, 01.06.2019

Gülden Eleyan , Ziya Telatar

https://doi.org/10.15317/Scitech.2019.213

Öz

In this study, feature extraction is performed
using Gabor wavelet transforms and Dual Tree wavelet transforms for face
detection. Artificial neural networks with feed forward are used in the
classification step. In the first of the proposed algorithms, the Dual Tree
feature extraction vectors are used to train the neural networks, while in the
second proposed algorithm, the Gabor feature extraction vectors are used in the
neural network training. The proposed third algorithm consists of combining the
perception results of the first two algorithms with OR logic operation. The
performance calculation of the system is realized with three metrics in which
the wrong perception rate is included in the account. Simulations were
performed on MIT + CMU, FRAV2D, BioID, BANCA databases. The dimensions of the
Gabor wavelet vectors are reduced to different ratios and the effects on the
processing time and performance are examined.

Anahtar Kelimeler

Dual-Tree Wavelets Transform, Face Detection, Gabor Wavelets Transform, Neural Network

KARMAŞIK DALGACIK DÖNÜŞÜMÜ TABANLI YAPAY SİNİR AĞLARI İLE YÜZ ALGILAMA

Öz

Bu
çalışmada yüz algılama için Gabor dalgacık dönüşümleri ve Çift Ağaç dalgacık
dönüşümleri kullanılarak öznitelik çıkarımı yapılmıştır. Sınıflandırma
basamağında ileri beslemeli yapay sinir ağları kullanılmıştır. Önerilen
algoritmaların ilkinde, sinir ağlarını eğitmek için Çift Ağaç öznitelik
vektörleri kullanılırken, ikincisinde sinir ağlarının eğitiminde Gabor
öznitelik vektörleri kullanılmaktadır. Önerilen üçüncü algoritma ise ilk iki algoritmanın
algı sonuçlarının OR mantık işlemi ile birleştirilmesinden oluşmaktadır. Sistemin
başarımı yanlış algı oranının da hesaba katıldığı üç metrik ile hesaplanmıştır.
MIT+CMU, FRAV2D,
BioID, BANCA veri
tabanları üzerinde simülasyonlar gerçekleştirilmiştir. Gabor dalgacık
vektörlerinin boyutları farklı oranlara indirgenerek işlem zamanı ve performans
üzerindeki etkileri incelenmiştir. 

Anahtar Kelimeler

Çift ağaç dalgacık dönüşümü, Gabor dalgacık dönüşümü, Sinir ağları, Yüz algılama

Kaynakça

• Acciani, G., Chiarantoni, E., Fornarelli, G. and Vergura, S. 2003. “A feature extraction unsupervised neural network for an environmental data set”, Neural Networks, Cilt 16, no 3-4, ss. 427-436.
• Eleyan, A., Ozkaramanli, H. and Demirel, H. 2009, “Dual-tree and single-tree complex wavelet transform based face recognition”, SIU 2009, Side, Turkey, ss. 536-539.
• Eleyan, A., Ozkaramanli, H. and Demirel, H. 2009, “Complex Wavelet Transform-Based Face Recognition“, EURASIP Journal on Advances in Signal Processing, Article ID 2008: 185281.
• Eleyan, A., Demirel, H. 2007. “PCA & LDA based Neural Networks for Human Face Recognition, in ''Face Recognition '', edited by K. Delac and M. Grgic , Intech, ss.93-106.
• Froba, B., Ernest, B. 2004. “Face detection with the modified census transform”, Proc. IEEE International Conferance on Automatic Face and gesture Recognition, ss. 91-96.
• Hongxia, J. 2007. “The application of neuro-FDT in urban short-term traffic flow prediction”, 3rd International Conference on Natural Computation, China, ss. 499-503.
• Huang, L.L, Shimizu, A., Kobatake, H., 2005, “Robust face detection using Gabor filter features”, Pattern Recognition Letters, Cilt 26, Sayı 11, ss. 1641-1649.
• Karahan M., 2015, “Turizm talebinin yapay sinir ağalari yöntemiyle tahmin edilmesi, Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi Y.2015, Cilt 20, Sayı 2, ss.195-209.
• Kim, J.K., Kang, S., 2017, “Neural network-based coronary heart disease risk prediction using feature correlation analysis. Journal of Healthcare Engineering. vol. 2017, Article ID 2780501.
• Kingsbury, N. G., 2003, “Design of Q-Shift Complex Wavelet for Image Processing Using Frequency Domain Energy Minimization”, Proceedings of IEEE International Conference on Image Processing (ICIP ‘03), Cilt 1, ss. 1013-1016.
• Lin, W.H., Wang, P., Tsai, C.F. 2016, “Face recognition using support vector model classifier for user authentication”, Electronic Commerce Research and Applications Cilt18, ss. 71-82.
• Liu, C. 2003, “A bayesian discriminating features method for face detection”, IEEE IEEE Transaction Pattern Analysis and Machine Intelligence, Cilt 25, Sayı 6. ss. 725-740.
• Liu, C. and Wechsler, H. 2003, “Independent component analysis of gabor features for face recognition”, IEEE Transaction on Neural Networks, Cilt 14, Sayı 4, ss. 919-928. Nanni, L. and Lumini, A. 2007, “Multi-expert approach for wavelet-based face detection”, Pattern Recognition Letters, Cilt 28, Sayı 12, ss. 1541-1547.
• Oh, B.S., Oh, K., Teoh, A.B.J., Lin, Z., Toh, K.A., 2017, “A Gabor-based network for heterogeneous face recognition”, Neurocomputing, Cilt 261, ss. 253-265.
• Ratsch, M., Romdhani, S. and Vetter, T. 2004, “Efficient face detection by a cascaded support vector machine using haar-like features. Proc. 26th Pattern Recognition Symposium, Tübingen, ss. 62–70.
• Rowley, H., Baluja, S. and Kanade, T. 1998, “Neural network-based face detection”, IEEE Pattern Analysis Machine Intelligent Transaction, Cilt 20, ss. 22–38.
• Schneiderman, H. and Kanade, T. 2000, “A statistical method for 3D object detection applied to faces and cars”, Proc. IEEE Conf. Computer Vision and Pattern Recognition, Cilt 1, ss. 746-751.
• Shah, P. M. 2012, “Face detection from images using support vector machine”, Yüksek lisans tezi. 321. San José State University.
• Sharma, M., Verma, P., Mathew, L., 2016, “Design an intelligent controller for a process control system”, International Conference on Innovation and Challenges in Cyber Security (ICICCS-INBUSH), Noida, ss. 217-223.
• Sung, K. K., Poggio, T. 1998, "Example-based learning for view-based human face detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, Cilt 20, Sayı 1, ss. 39-51.
• Telatar, Z., Sazlı, M.H., Muhammad I., 2007, “Neural network based face detection from pre-scanned and row-column decomposed average face image”, ACIVS 2007, LNCS 4678, ss. 297–309.
• Türkeç, M.B. 2007, Bayes sınıflandırıcı kullanarak yüz sezimi. Yüksek lisans tezi, Hacettepe üniversitesi, 91 s., Ankara.
• Tsai, C.C., Cheng, W.C., Taur, J.S., Tao, C.W. 2006, “Face detection using eigenfaces ad neural network”, Proc. IEEE Int’l Conf. on Systems, Man, Cybernetics, ss. 4343-4347.
• Turk, M., Pentland, A. 1991. Eigenfaces for Recognition. Journal of Cognitive Neuroscience, Cilt 3, Sayı 1, ss. 71-86.
• Yang, G., Huang, T. S. 1994, “Human face detection in complex background”, Pattern Recognition, Cilt 27, Sayı 1, ss. 53-63.