Derin Sinir Ağlarını Kullanarak İnsan Yüzü Tanıma

Year 2022, Volume: 4 Issue: 2, 189 - 198, 26.10.2022

Kaplan Kaplan , Fatma Kuncan

https://doi.org/10.46387/bjesr.1139029

Abstract

Son yıllarda birçok araştırmacı farklı uygulamalar için yapay zeka uygulamalarını içeren bilgisayar tabanlı sistemler kullanmaktadır. Kişi tanıma uygulaması da bu alanda yapılan çalışmalardandır. İlk zamanlarda güvenlik önlemleri için tasarlanan yüz ve nesne tanıma uygulamaları, son zamanlarda eğlence ve alışveriş sektörü alanlarında da kullanılmaktadır. Bu uygulamalar, çeşitli firmaların mobil uygulama geliştirmeleriyle daha da popülerlik kazanmaktadır. Yüz tanıma uygulamalarında, verilerin büyük ve karmaşık olması durumunda derin öğrenme yöntemleri tercih edilebilmektedir. Bu çalışmada da bir yüz tanıma uygulaması için 3 katmanlı bir Evrişimli Sinir Ağı (ESA) geliştirilmiştir. Geliştirilen model Libor Spacek's Facial Images Databases veri setine uygulanmıştır. Önerilen yöntemin veri seti üzerine uygulanması sonucunda %99.29 doğruluk oranı olduğu belirlenmiştir. Bu da uygulamanın gerçek bir tanıma sistemine uyarlanabileceği anlamına gelmektedir.

Keywords

Derin öğrenme, Evrişimli Sinir Ağları, Yüz Tanıma, Faces96

Human Face Recognition Using Deep Neural Networks

Abstract

In recent years, many researchers have been using computer-based systems containing artificial intelligence applications for different applications. Human recognition application is one of the studies carried out in this field. Face and object recognition applications, which were originally designed for security measures, are also used in the entertainment and shopping sectors recently. These applications are gaining even more popularity with the mobile application development of various companies. In face recognition applications, deep learning methods can be preferred if the data is large and complex. In this study, a 3-layer Convolutional Neural Network (CNN) has been developed for a face recognition application. The developed model was applied to the Libor Spacek's Facial Images Databases dataset. As a result of the application of the proposed method on the data set, it was determined that the accuracy rate was 99.29%. This means that the application can be adapted for real recognition systems.

Keywords

Deep Learning, Convolutional Neural Networks, Face Recognition, Faces96

References

• [1] A. K. Jain, K. Nandakumar, A. Ross, “50 Years of Biometric Research: Accomplishments, Challenges, and Opportunities”, Pattern Recognition Letters, vol. no. 79, pp. 80-105, 2016.
• [2] A. K. Jain, A. Ross, S. Prabhakar, “An Introduction to Biometric Recognition”, IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4-20, 2004.
• [3] W. W. Bledsoe, “The Model Method In Facial Recognition. Panoramic Research Inc.”, Palo Alto, CA, Rep. PR1, vol. 15, no. 47, pp. 2, 1966.
• [4] A. J. Goldstein, L. D. Harmon, A. B. Lesk, “Identification of Human Faces”, Proceedings of the IEEE”, vol. 59, no.5, pp. 748-760, 1971.
• [5] M. Naeem, I. Qureshi, F. Azam, “Face Recognition Techniques And Approaches: A Survey”, Science International, vol. 27, pp. 1, 2015.
• [6] T. Kanade, “Picture Processing System By Computer Complex and Recognition of Human Faces”, Basel: Birkhäuser, vol. 47, p. 63, 1974.
• [7] C. G. Gross, J. Sergent, “Face Recognition”, Current Opinion In Neurobiology, vol 2, no. 2, pp. 156-161, 1992.
• [8] W. Zhao, R. Chellappa, P. J. Phillips, A. Rosenfeld, “Face recognition: A literature survey”, ACM Computing Surveys (CSUR), vol. 35, no. 4, pp. 399-458, 2003.
• [9] A. S. Tolba, A. H. El-Baz, A. A. El-Harby, “Face recognition: A literature review”, International Journal of Signal Processing, vol. 2, no. 2, pp. 88-103, 2006.
• [10] M. A. Rahim, M. S. Azam, N. Hossain, M. R. Islam, “Face Recognition Using Local Binary Patterns (LBP)”, Global Journal of Computer Science and Technology, vol. 13, no.4, pp. 1-9, 2013.
• [11] M. Mehdipour Ghazi, H. Kemal Ekenel, “A Comprehensive Analysis Of Deep Learning Based Representation for Face Recognition”, Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 34-41, 2016.
• [12] E. U. Haq, X. Huarong, M. I. Khattak, “Notice of Retraction: Face Recognition by SVM Using Local Binary Patterns”, 14th Web Information Systems and Applications Conference (WISA), pp. 172-175, 2017.
• [13] X. Qu, T. Wei, C. Peng & P. Du, “A Fast Face Recognition System Based on Deep Learning”, 11th International Symposium on Computational Intelligence and Design (ISCID), pp. 289-292, 2018.
• [14] H. Öziş, İ. Kandilli, M. Kuncan, M. “Face Recognition System With Raspberry Pi”, 4th International Zeugma on Scientific Researches, pp. 178-185, 2020.
• [15] G. Bolukbaş, E. Başaran, M. E. Kamaşak, “BMI Prediction From Face Images”. 27th Signal Processing and Communications Applications Conference (SIU), pp. 1-4, 2019.
• [16] G. Guo, Z. Z. Li, K. Chan, “Face recognition by support vector machines”, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 196-201, 2000.
• [17] M. Agarwal, H. Agrawal, N. Jain, M. Kumar, “Face recognition using principle component analysis, eigenface and neural network”, 2010 International Conference on Signal Acquisition and Processing, pp. 310-314, 2010.
• [18] N. P. Ramaiah, E. P. Ijjina, C. K. Mohan, “Illumination invariant face recognition using convolutional neural networks”, International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), pp. 1-4, 2015.
• [19] H. El Khiyari, H. Wechsler, H. “Face recognition across time lapse using convolutional neural networks”, Journal of Information Security, vol. 7, no. 3, pp. 141-151, 2016.
• [20] P. Kamencay, M. Benco, T. Mizdos, R. Radil, “A new method for face recognition using convolutional neural network”, Advances in Electrical and Electronic Engineering, 15(4), 663-672.
• [21] M. Coşkun, A. Uçar, A., Ö. Yildirim, Y. Demir, “Face recognition based on convolutional neural network”, In 2017 International Conference on Modern Electrical and Energy Systems (MEES), pp. 376-379, 2017.
• [22] M. F. Hansen, M. L. Smith, L. N. Smith, M. G. Salter, E. M. Baxter, M. Farish, B. Grieve, “Towards on-farm pig face recognition using convolutional neural networks”, Computers in Industry, vol. 98, pp. 145-152, 2018.
• [23] S. Khan, M. H. Javed, E. Ahmed, S. A. Shah, S. U. Ali, “Facial recognition using convolutional neural networks and implementation on smart glasses”, 2019 International Conference on Information Science and Communication Technology (ICISCT), pp. 1-6, 2019.
• [24] M. Zulfiqar, F. Syed, M. J. Khan, K. Khurshid, “Deep face recognition for biometric authentication”, In 2019 international conference on electrical, communication, and computer engineering (ICECCE), pp. 1-6, 2019.
• [25] R. Abinaya, L. P. Maguluri, S. Narayana, M. Syamala, “A novel biometric approach for facial image recognition using deep learning techniques”, International Journal of Advanced Research in Engineering and Technology, vol. 11, no. 9, 2020.
• [26] S. Sharma, M. Bhatt, P. Sharma, “Face recognition system using machine learning algorithm”, 5th International Conference on Communication and Electronics Systems (ICCES), pp. 1162-1168, 2020.
• [27] D. Hond, L. Spacek, “Distinctive Descriptions for Face Processing”, The British Machine Vision Conference (BMVC), pp. 1-4, 1997.
• [28] Y. LeCun, Y. Bengio, G. Hinton, “Deep learning” Nature, vol. 521, no. 7553, pp. 436-444., 2015.
• [29] I. Goodfellow, Y. Bengio, A. Courville, “Deep learning”, 1st ed., London, UK, MIT press, 2016.
• [30] C. Ying, M. Qi-Guang, L. Jia-Chen, and G. Lin, “Advance and Prospects of AdaBoost Algorithm”. Acta Automatica Sinica, vol. 39, no. 6, pp. 745-758, 2013.
• [31] M. Paluszek, S. Thomas, “Practical MATLAB Deep Learning: A Project-Based Approach”, 1st ed., New Jersey, USA, Apress, 2020.