Volume 15, Issue 1 (Special Issue on AI in ICT 2023)                   itrc 2023, 15(1): 16-23 | Back to browse issues page


XML Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Navid Mahmoudabadi N, Afshar Kazemi M A, Radfar R, Amin Mousavi S A. A New Fuzzy Convolutional Neural Network for Face Recognition to Classify Authorized and Unauthorized Persons. itrc 2023; 15 (1) : 2
URL: http://journal.itrc.ac.ir/article-1-507-en.html
1- Department of Information Technology Management Qeshm branch, Islamic Azad University Qeshm,Iran n.mahmoodabadi@gmail.com
2- Department of Industrial Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran , dr.mafshar@gmail.com
3- Department of Industrial Management, Islamic Azad University, Science and Research Branch, Tehran, Iran
4- Department of Information Technology Management, Islamic Azad University, Science and Research Branch, Tehran, Iran
Abstract:   (1542 Views)

Deep learning methods use neural networks that try to discover patterns within the image without human intervention and to learn that. One of the most popular algorithms in this field is the convolutional neural network algorithm. This algorithm uses several layers to receive the input image and process it so that the class label can be found. These layers are mostly based on Neural Networks. This research aims to provide a model of neural-fuzzy, based on convolutional neural network algorithm. In this research, we use the positive advantages of deep learning methods and fuzzy inference systems and present a new model of their application to Classify authorized and unauthorized Persons. For this purpose, we designed new neural-fuzzy layers to pass the image through them and finally classify each image. The results of the implementation of the above model show the efficiency and success of this system.

Article number: 2
Full-Text [PDF 800 kb]   (830 Downloads)    
Type of Study: Research | Subject: Information Technology

References
1. [1] Huble, D. H., & Wiesel, T. N. (1968). Receptive fields and functional architecture of monkey striate cortex. The Journal of Physiology, 215-243.
2. [2] Cohen, A. I. (1972). Rods and Cones. In Physiology of Photoreceptor Organs (pp. 63-110). Springer Berlin Heidelberg.
3. [3] Panchal, G., Amit, G., P., K. Y., & Devyani, P. (2011). Behaviour Analysis of Multilayer Perceptronswith Multiple Hidden Neurons and Hidden Layers. International Journal of Computer Theory and Engineering, 332-337.
4. [4] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436-444.
5. [5] D. A. R. Wati and D. Abadianto, "Design of face detection and recognition system for smart home security application," 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE),
6. 2017, pp. 342-347, doi: 10.1109/ICITISEE.2017.8285524.
7. [6] D. A. Chowdhry, A. Hussain, M. Z. Ur Rehman, F. Ahmad, A. Ahmad and M. Pervaiz, "Smart security system for sensitive area using face recognition," 2013 IEEE Conference on Sustainable Utilization and Development in Engineering and
8. Technology (CSUDET), 2013, pp. 11-14, doi: 10.1109/CSUDET.2013.6670976.
9. [7] Ciresan, D. C., Meier, U., Masci, J., Maria Gambardella, L., & Schmidhuber, J. (2011). Flexible, high performance convolutional neural networks for image classification. the International Joint Conference on Artificial Intelligence (pp.
10. 1242). California: AAAI Press. [8] Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. in Proceedings of the IEEE, 86(11), 2278-2324.
11. [9] LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., et al. (1989). Handwritten digit recognition with a back-propagation network. Advances in neural information processing systems, 396-404.
12. [10] Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science,313(5786), 504-507.
13. [11] Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2006).Greedy layer-wise training of deep networks. Advances in neural information processing systems, 2814-2822.
14. [12] Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks.Advances in neural information processing systems, 25.
15. [13] Deng, L. (2014). A tutorial survey of architectures, algorithms, and applications for deep learning. APSIPA Transactions on Signal and Information Processing, 3(2), 1-29.
16. [14] Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., et al. (2015). ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3),211-252.
17. Volume 15- Number 1 - 2023 (16 -23) 22
18. [15] Simonyan, K., & Zisserman. (2014). Very deep convolutional networks for large-scale image recognition. ICLR.
19. [16] Zeiler, M. D., & Fergus, R. (2013). Stochastic Pooling for Regularization of Deep Convolutional Neural Networks.arXiv:1301.3557.
20. [17] Gong, Y., Wang, L., Guo, R., & Lazebnik, S. (2014). Multiscale orderless pooling of deep convolutional activation features. the European Conference on Computer Vision (pp.392-407). Berlin: Springer.
21. [18] Szegedy, C., Ioffe, S., & Vanhoucke, V. (2016). Inception-v4,Inception-Resnet and the impact of residual connections on learning. arXiv 1602.07261.
22. [19] He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep residual learning for image recognition. arXiv 1512.03385.
23. [20] Zhao, Q., & Griffin, L. D. (2016). Suppressing the unusual: Towards robust CNNs using symmetric activation functions .arXiv 1603.05145v1.
24. [21] Tang, Y. (2013). Deep learning using linear support vector machines. arXiv 1306.0239.
25. [22] Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv1207.0580.
26. [23] Taigman, Y., Yang, M., Ranzato, M., & Wolf, L. (2014). DeepFace: Closing the Gap to Human-Level Performance in Face Verification. IEEE Conference on Computer Vision and Pattern Recognition, (pp. 1701-1708). Columbus.
27. [24] Bengio, Y. (2009). Learning deep architectures for AI.Foundations and Trends in Machine Learning, 2(1), 1-127.
28. [25] Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.
29. [26] Deng, L., & Yu, D. (2014). Deep learning: Methods and applications. Foundations and Trends in Signal Processing,197-387.
30. [27] Hubel, D. H. (1959). Receptive fields of single neurones in the cat's striate cortex. Journal of Physiology, 148(1), 574-591.
31. [28] Hubel, D. H. (1962). Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. Journal of Physiology, 160(1), 106-154.
32. [29] Yu, D., Wang, H., Chen, P., & Wei, Z. (2014). Mixed pooling for convolutional neural networks. the 9th International Conference on Rough Sets and Knowledge Technology (pp.364-375). Berlin: Springer.
33. [30] Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted Boltzmann machines. the 27th international conference on machine learning, (pp. 807-814).
34. [31] Thikshaja, U. K., & Paul, A. (2017). A Brief Review on Deep Learning and Types of Implementation for Deep Learning. In S. Karthik, A. Paul, & N. Karthikeyan, Deep Learning Innovations and Their Convergence With Big Data (pp. 20-32).
35. IGI Global.
36. [32] Yu, W., Yang, K., Bai, Y., Yao, H., & Rui, Y. (2014). Visualizing and Comparing Convolutional Neural Networks. Computer Vision and Pattern Recognition.
37. [33] Srinivas, S., Sarvadevabhatla, R. K., Mopuri, K. R., Prabhu, N.,Kruthiventi, S. S., & Babu, R. V. (2016). A taxonomy of deep convolutional neural nets for computer vision. Frontiers in Robotics and AI .
38. [34] Buduma, N. (2017). Fundamentals of Deep Learning. O'Reilly Media, Inc.
39. [35] Denyer, S. (2018). Beijing bets on facial recognition in a big drive for total surveillance. washingtonpost.
40. [36] Wiatowski, T., & Bölcskei, H. (2018). A mathematical theory of deep convolutional neural networks for feature extraction.IEEE Transactions on Information Theory, 64(3), 1845-1866.
41. [37] Scherhag, U., Rathgeb, C., Merkle, J., Breithaupt, R., & Busch, C. (2019). Face Recognition Systems Under Morphing Attacks: A Survey. IEEE Access, 7, 23012-23026.
42. [38] F. Schroff, D. Kalenichenko and J. Philbin, "FaceNet: A Unified Embedding for Face Recognition and Clustering", 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 815-823, 2015.
43. [39] I. William, D. R. Ignatius Moses Setiadi, E. H. Rachmawanto, H. A. Santoso and C. A. Sari, "Face Recognition using FaceNet (Survey, Performance Test, and Comparison)," 2019 Fourth
44. International Conference on Informatics and Computing (ICIC), 2019, pp. 1-6, doi: 10.1109/ICIC47613.2019.8985786 [40] D. Yi, Z. Lei, S. Liao, and S. Z. Li, "Learning Face Representation from Scratch," 2014.
45. [41] Q. Cao, L. Shen, W. Xie, O. M. Parkhi and A. Zisserman, "VGGFace2: A dataset for recognising faces across pose and age," International Conference on Automatic Face and Gesture Recognition, 2018.

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author


Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.