Araştırma Makalesi
Yıl 2019,
Cilt: 61 Sayı: 2, 129 - 149, 01.12.2019
Öz
Kaynakça
- Tolba, A. S., A. H. El-Baz, and A. A. El-Harby. "Face recognition: A literature review." International Journal of Signal Processing 2.2 (2006): 88-103.
- Sharif, Muhammad, Sajjad Mohsin, and Muhammad Younas Javed. "A survey: face recognition techniques." Research Journal of Applied Sciences, Engineering and Technology 4.23 (2012): 4979-4990.
- LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436.
- Elgallad, Elaraby A., et al. "Human identity recognition using sparse auto encoder for texture information representation in palmprint images based on voting technique." Computer Science and Information Technology (SCCSIT), 2017 Sudan Conference on. IEEE, 2017.
- Anar, Ali Canberk, Erkan Bostanci, and Mehmet Serdar Guzel. "Live Target Detection with Deep Learning Neural Network and Unmanned Aerial Vehicle on Android Mobile Device." arXiv preprint arXiv:1803.07015.2018.
- LeCun, Yann, Koray Kavukcuoglu, and Clément Farabet. "Convolutional networks and applications in vision." Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on. IEEE, 2010.
- Mikolov, Tomáš, et al. "Strategies for training large scale neural network language models." Automatic Speech Recognition and Understanding (ASRU), 2011 IEEE Workshop on. IEEE, 2011.
- Collobert, Ronan, et al. "Natural language processing (almost) from scratch." Journal of Machine Learning Research 12.Aug (2011): 2493-2537.
- Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
- Farabet, Clement, et al. "Learning hierarchical features for scene labeling." IEEE transactions on pattern analysis and machine intelligence 35.8 (2013): 1915-1929.
- Tompson, Jonathan J., et al. "Joint training of a convolutional network and a graphical model for human pose estimation." Advances in neural information processing systems. 2014.
- Bordes, Antoine, Sumit Chopra, and Jason Weston. "Question answering with subgraph embeddings." arXiv preprint arXiv:1406.3676 (2014).
- Sun, Yi, Xiaogang Wang, and Xiaoou Tang. "Deep learning face representation from predicting 10,000 classes." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014.
- Taigman, Yaniv, et al. "Deepface: Closing the gap to human-level performance in face verification." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
- Schroff, Florian, Dmitry Kalenichenko, and James Philbin. "Facenet: A unified embedding for face recognition and clustering." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
- Setiowati, Sulis, Eka Legya Franita, and Igi Ardiyanto. "A review of optimization method in face recognition: Comparison deep learning and non-deep learning methods." Information Technology and Electrical Engineering (ICITEE), 2017 9th International Conference on. IEEE, 2017.
- Sun, Yi, et al. "Deep learning face representation by joint identification-verification." Advances in neural information processing systems. 2014.
- Yi, Dong, et al. "Learning face representation from scratch." arXiv preprint arXiv:1411.7923 (2014).
- Parkhi, Omkar M., Andrea Vedaldi, and Andrew Zisserman. "Deep Face Recognition." BMVC. Vol. 1. No. 3. 2015.
- Ahmed, Ejaz, Michael Jones, and Tim K. Marks. "An improved deep learning architecture for person re-identification." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
- Sun, Yi, et al. "Deepid3: Face recognition with very deep neural networks." arXiv preprint arXiv:1502.00873 (2015).
- Szegedy, Christian, et al. "Going deeper with convolutions." Cvpr, 2015.
- Chen, Yanbei, Xiatian Zhu, and Shaogang Gong. "Person re-identification by deep learning multi-scale representations." (2018).
- Xiao, Tong, et al. "Learning deep feature representations with domain guided dropout for person re-identification." Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on. IEEE, 2016.
- Viola, Paul, and Michael J. Jones. "Robust real-time face detection." International journal of computer vision 57.2 (2004): 137-154.
- Dalal, Navneet, and Bill Triggs. "Histograms of oriented gradients for human detection." Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. Vol. 1. IEEE, 2005.
- Cortes, Corinna, and Vladimir Vapnik. "Support-vector networks." Machine learning 20.3 (1995): 273-297.
- Li, Xiang-Yu, and Zhen-Xian Lin. "Face Recognition Based on HOG and Fast PCA Algorithm." The Euro-China Conference on Intelligent Data Analysis and Applications. Springer, Cham, 2017.
- Albiol, Alberto, et al. "Face recognition using HOG–EBGM." Pattern Recognition Letters 29.10 (2008): 1537-1543.
- Déniz, Oscar, et al. "Face recognition using histograms of oriented gradients." Pattern Recognition Letters 32.12 (2011): 1598-1603.
- Peker, Murat, Halis Altun, and Fuat Karakaya. "HOG Temelli Bir Yöntem ile Ölçek ve Yönden Bağımsız Gerçek Zamanlı Nesne Tanıma."
- Phillips, P. Jonathon. "Support vector machines applied to face recognition." Advances in Neural Information Processing Systems. 1999.
- Ayhan, Sevgi, and Şenol Erdoğmuş. "Destek vektör makineleriyle sınıflandırma problemlerinin çözümü için çekirdek fonksiyonu seçimi." Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi 9.1 (2014).
- Heisele, Bernd, Purdy Ho, and Tomaso Poggio. "Face recognition with support vector machines: Global versus component-based approach." Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on. Vol. 2. IEEE, 2001.
- Wang, Zhe, and Xiangyang Xue. "Multi-class support vector machine." Support Vector Machines Applications. Springer, Cham, 2014. 23-48.
- Wu, Jianxin. "Introduction to convolutional neural networks." National Key Lab for Novel Software Technology. Nanjing University. China (2017).
- O'Shea, Keiron, and Ryan Nash. "An introduction to convolutional neural networks." arXiv preprint arXiv:1511.08458 (2015).
- Grm, Klemen, et al. "Strengths and weaknesses of deep learning models for face recognition against image degradations." IET Biometrics 7.1 (2017): 81-89.
- Pilla Jr, Valfredo, et al. "Facial Expression Classification Using Convolutional Neural Network and Support Vector Machine."
- Ghazi, Mostafa Mehdipour, and Hazim Kemal Ekenel. "A comprehensive analysis of deep learning based representation for face recognition." arXiv preprint arXiv:1606.02894 (2016).
- Lu, Ze, Xudong Jiang, and Alex Kot. "Enhance deep learning performance in face recognition." Image, Vision and Computing (ICIVC), 2017 2nd International Conference on. IEEE, 2017.
- Samaria, Ferdinando S., and Andy C. Harter. "Parameterisation of a stochastic model for human face identification." Applications of Computer Vision, 1994., Proceedings of the Second IEEE Workshop on. IEEE, 1994.
- Hond, Darryl, and Libor Spacek. "Distinctive Descriptions for Face Processing." BMVC. No. 0.2. 1997.
- Bostanci, Betul, and Erkan Bostanci. "An evaluation of classification algorithms using Mc Nemar’s test." Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Springer, India, 2013.
A COMPARISON OF DEEP LEARNING BASED ARCHITECTURE WITH A CONVENTIONAL APPROACH FOR FACE RECOGNITION PROBLEM
Öz
This
paper addresses a new approach for face recognition problem based on deep
learning strategy. In order to verify the performance of the proposed approach,
it is compared with a conventional face recognition method by using various
comprehensive datasets. The conventional
approach employs Histogram of Gradient (HOG) algorithm to extract features and
utilizes a multi-class Support Vector Machine (SVM) classifier to train and
learn the classification. On the other hand, the proposed deep learning based
approaches employ a Convolutional Neural Network (CNN) based architecture and
also offer both a SVM and Softmax classifiers respectively for the
classification phase. Results reveal that the proposed deep learning
architecture using Softmax classifier outperform conventional method by a
substantial margin. As well as, the deep learning architecture using Softmax
classifier also outperform SVM in almost all cases.
Anahtar Kelimeler
Convolutional Neural Network Deep Learning Face Recognition Fine Tuning Softmax
Kaynakça
- Tolba, A. S., A. H. El-Baz, and A. A. El-Harby. "Face recognition: A literature review." International Journal of Signal Processing 2.2 (2006): 88-103.
- Sharif, Muhammad, Sajjad Mohsin, and Muhammad Younas Javed. "A survey: face recognition techniques." Research Journal of Applied Sciences, Engineering and Technology 4.23 (2012): 4979-4990.
- LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436.
- Elgallad, Elaraby A., et al. "Human identity recognition using sparse auto encoder for texture information representation in palmprint images based on voting technique." Computer Science and Information Technology (SCCSIT), 2017 Sudan Conference on. IEEE, 2017.
- Anar, Ali Canberk, Erkan Bostanci, and Mehmet Serdar Guzel. "Live Target Detection with Deep Learning Neural Network and Unmanned Aerial Vehicle on Android Mobile Device." arXiv preprint arXiv:1803.07015.2018.
- LeCun, Yann, Koray Kavukcuoglu, and Clément Farabet. "Convolutional networks and applications in vision." Circuits and Systems (ISCAS), Proceedings of 2010 IEEE International Symposium on. IEEE, 2010.
- Mikolov, Tomáš, et al. "Strategies for training large scale neural network language models." Automatic Speech Recognition and Understanding (ASRU), 2011 IEEE Workshop on. IEEE, 2011.
- Collobert, Ronan, et al. "Natural language processing (almost) from scratch." Journal of Machine Learning Research 12.Aug (2011): 2493-2537.
- Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Advances in neural information processing systems. 2012.
- Farabet, Clement, et al. "Learning hierarchical features for scene labeling." IEEE transactions on pattern analysis and machine intelligence 35.8 (2013): 1915-1929.
- Tompson, Jonathan J., et al. "Joint training of a convolutional network and a graphical model for human pose estimation." Advances in neural information processing systems. 2014.
- Bordes, Antoine, Sumit Chopra, and Jason Weston. "Question answering with subgraph embeddings." arXiv preprint arXiv:1406.3676 (2014).
- Sun, Yi, Xiaogang Wang, and Xiaoou Tang. "Deep learning face representation from predicting 10,000 classes." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014.
- Taigman, Yaniv, et al. "Deepface: Closing the gap to human-level performance in face verification." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014.
- Schroff, Florian, Dmitry Kalenichenko, and James Philbin. "Facenet: A unified embedding for face recognition and clustering." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
- Setiowati, Sulis, Eka Legya Franita, and Igi Ardiyanto. "A review of optimization method in face recognition: Comparison deep learning and non-deep learning methods." Information Technology and Electrical Engineering (ICITEE), 2017 9th International Conference on. IEEE, 2017.
- Sun, Yi, et al. "Deep learning face representation by joint identification-verification." Advances in neural information processing systems. 2014.
- Yi, Dong, et al. "Learning face representation from scratch." arXiv preprint arXiv:1411.7923 (2014).
- Parkhi, Omkar M., Andrea Vedaldi, and Andrew Zisserman. "Deep Face Recognition." BMVC. Vol. 1. No. 3. 2015.
- Ahmed, Ejaz, Michael Jones, and Tim K. Marks. "An improved deep learning architecture for person re-identification." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.
- Sun, Yi, et al. "Deepid3: Face recognition with very deep neural networks." arXiv preprint arXiv:1502.00873 (2015).
- Szegedy, Christian, et al. "Going deeper with convolutions." Cvpr, 2015.
- Chen, Yanbei, Xiatian Zhu, and Shaogang Gong. "Person re-identification by deep learning multi-scale representations." (2018).
- Xiao, Tong, et al. "Learning deep feature representations with domain guided dropout for person re-identification." Computer Vision and Pattern Recognition (CVPR), 2016 IEEE Conference on. IEEE, 2016.
- Viola, Paul, and Michael J. Jones. "Robust real-time face detection." International journal of computer vision 57.2 (2004): 137-154.
- Dalal, Navneet, and Bill Triggs. "Histograms of oriented gradients for human detection." Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. Vol. 1. IEEE, 2005.
- Cortes, Corinna, and Vladimir Vapnik. "Support-vector networks." Machine learning 20.3 (1995): 273-297.
- Li, Xiang-Yu, and Zhen-Xian Lin. "Face Recognition Based on HOG and Fast PCA Algorithm." The Euro-China Conference on Intelligent Data Analysis and Applications. Springer, Cham, 2017.
- Albiol, Alberto, et al. "Face recognition using HOG–EBGM." Pattern Recognition Letters 29.10 (2008): 1537-1543.
- Déniz, Oscar, et al. "Face recognition using histograms of oriented gradients." Pattern Recognition Letters 32.12 (2011): 1598-1603.
- Peker, Murat, Halis Altun, and Fuat Karakaya. "HOG Temelli Bir Yöntem ile Ölçek ve Yönden Bağımsız Gerçek Zamanlı Nesne Tanıma."
- Phillips, P. Jonathon. "Support vector machines applied to face recognition." Advances in Neural Information Processing Systems. 1999.
- Ayhan, Sevgi, and Şenol Erdoğmuş. "Destek vektör makineleriyle sınıflandırma problemlerinin çözümü için çekirdek fonksiyonu seçimi." Eskişehir Osmangazi Üniversitesi İktisadi ve İdari Bilimler Dergisi 9.1 (2014).
- Heisele, Bernd, Purdy Ho, and Tomaso Poggio. "Face recognition with support vector machines: Global versus component-based approach." Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on. Vol. 2. IEEE, 2001.
- Wang, Zhe, and Xiangyang Xue. "Multi-class support vector machine." Support Vector Machines Applications. Springer, Cham, 2014. 23-48.
- Wu, Jianxin. "Introduction to convolutional neural networks." National Key Lab for Novel Software Technology. Nanjing University. China (2017).
- O'Shea, Keiron, and Ryan Nash. "An introduction to convolutional neural networks." arXiv preprint arXiv:1511.08458 (2015).
- Grm, Klemen, et al. "Strengths and weaknesses of deep learning models for face recognition against image degradations." IET Biometrics 7.1 (2017): 81-89.
- Pilla Jr, Valfredo, et al. "Facial Expression Classification Using Convolutional Neural Network and Support Vector Machine."
- Ghazi, Mostafa Mehdipour, and Hazim Kemal Ekenel. "A comprehensive analysis of deep learning based representation for face recognition." arXiv preprint arXiv:1606.02894 (2016).
- Lu, Ze, Xudong Jiang, and Alex Kot. "Enhance deep learning performance in face recognition." Image, Vision and Computing (ICIVC), 2017 2nd International Conference on. IEEE, 2017.
- Samaria, Ferdinando S., and Andy C. Harter. "Parameterisation of a stochastic model for human face identification." Applications of Computer Vision, 1994., Proceedings of the Second IEEE Workshop on. IEEE, 1994.
- Hond, Darryl, and Libor Spacek. "Distinctive Descriptions for Face Processing." BMVC. No. 0.2. 1997.
- Bostanci, Betul, and Erkan Bostanci. "An evaluation of classification algorithms using Mc Nemar’s test." Proceedings of Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012). Springer, India, 2013.
Toplam 44 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Mühendislik |
| Bölüm | Review Articles |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Aralık 2019 |
| Gönderilme Tarihi | 20 Şubat 2019 |
| Kabul Tarihi | 12 Haziran 2019 |
| Yayımlandığı Sayı | Yıl 2019 Cilt: 61 Sayı: 2 |
Kaynak Göster
Cited By
Identification with Face Recognition Methods in Real Life Applications
International Journal of Engineering Technologies IJET
https://doi.org/10.19072/ijet.817959
Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.