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GAN'lar Kullanılarak Artırılmış Yüz Görüntülerinde Ayrık Kosinüs Dönüşümü Yoluyla Görüntü Sınıflandırma Performansının Artırılması

Year 2023, Volume: IDAP-2023 : International Artificial Intelligence and Data Processing Symposium Issue: IDAP-2023, 7 - 18, 18.10.2023
https://doi.org/10.53070/bbd.1361811

Abstract

Teknolojideki sürekli ilerlemeler, yapay zeka alanı da dahil olmak üzere çeşitli alanları derinden etkilemektedir. Bu alanda yüz tanıma sistemlerinin geliştirilmesi ve eğitimi öne çıkan araştırma alanlarından biri olarak ortaya çıkmıştır. Günümüzde yüz tanıma sistemleri hızla geleneksel güvenlik yöntemlerinin yerini alıyor. İyi bir yüz tanıma sisteminin geliştirilebilmesi için eğitim sürecine yeterli verinin sağlanması gerekmektedir. Son zamanlarda yüz tanıma sistemlerinin doğruluğunu artırmaya yardımcı olabilecek açık kaynaklı verilerin sayısı sınırlıdır. Üretken Çekişmeli Ağlar (GAN'lar), rekabetçi bir ilişki içinde olan birbirine bağlı iki sinir ağından oluşan bir tür makine öğrenme algoritmasıdır. Görüntü oluşturma, görüntü işleme, süper çözünürlük, metin görselleştirme, fotogerçekçi görüntüler, konuşma üretimi ve yüz yaşlandırma gibi çalışma alanlarında yaygın olarak kullanılmaktadır. Çalışmada yüz tanıma sistemlerinin eğitimi için veri eksikliği ilk olarak GAN'lar ile elde edilen sentetik yüz görüntüleri ile giderilmiştir. Araştırmanın sonraki aşamasında, görüntülere ayrık kosinüs dönüşümünün uygulanması yoluyla görüntü sınıflandırma prosedürünün geliştirilmesi amaçlandı. Bu yaklaşım, yüz tanıma sistemlerini sanal ortamlarda özgün görünen sahte yüzlerin varlığına karşı güçlendirmeyi amaçlıyordu. Çalışmada yüzlerin sınıflandırılmasının normal sınıflandırma modeline göre %30 oranında iyileştirilebildiği tespit edildi. Bu araştırma çabasının temel amacı, yüksek doğruluklu yüz tanıma sistemlerinin geliştirilmesine önemli bir katkı sağlamaktır.

References

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  • Ullah, R., Hayat, H., Siddiqui, A. A., Siddiqui, U. A., Khan, J., Ullah, F., ... & Karami, G. M. (2022). A real-time framework for human face detection and recognition in cctv images. Mathematical Problems in Engineering
  • Obaida, T. H., Jamil, A. S., & Hassan, N. F. (2022). Real-time face detection in digital video-based on Viola-Jones supported by convolutional neural networks. International Journal of Electrical & Computer Engineering (2088-8708), 12(3)
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  • Tahir, F. S., Abdulrahman, A. A., & Hikmet Thanon, Z. (2022). Novel face detection algorithm with a mask on neural network training. International Journal of Nonlinear Analysis and Applications, 13(1), 209-215
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  • Öztürk, E., & Kurnaz, Ç. (2020). Görünüm Tabanlı Yüz Tanıma Yöntemleri Kullanılarak Cinsiyet Belirleme. Avrupa Bilim ve Teknoloji Dergisi, 111-120
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  • Adhinata, F., & Junaidi, A. (2022). Gender classification on video using FaceNet algorithm and supervised machine learning. International Journal of Computing and Digital Systems, 11(1), 199-208
  • Tao, X., & Pan, D. (2022). Face recognition based on scale invariant feature transform and fuzzy reasoning. Internet Technology Letters, e346
  • Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of big data, 6(1), 1-48
  • Alimovski, E. (2019). Derin öğrenmeye dayalı güçlü yüz tanıma sistemi için gan ile veri çoğaltma (Master's thesis, İstanbul Sabahattin Zaim Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Anabilim Dalı)
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  • C.I. a. P.Lab . Real and Fake Face Detection (ed.). Available: https://www.kaggle.com/ciplab/real-and-fake-facedetection (2019)
  • Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., ... & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern recognition, 77, 354-377
  • Min, S., Lee, B., & Yoon, S. (2017). Deep learning in bioinformatics. Briefings in bioinformatics, 18(5), 851-869
  • Hanbay, K. (2020). Hyperspectral image classification using convolutional neural network and two-dimensional complex Gabor transform. Journal of the Faculty of Engineering and Architecture of Gazi University, 35(1), 443-456
  • Toğaçar, M., Ergen, B., & Özyurt, F. (2020). Evrişimsel sinir ağı modellerinde özellik seçim yöntemlerini kullanarak çiçek görüntülerinin sınıflandırılması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32(1), 47-56.
  • Mateen, M., Wen, J., Nasrullah, Song, S., & Huang, Z. (2018). Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry, 11(1), 1
  • Mostafiz, R., Rahman, M. M., Islam, A. K., & Belkasim, S. (2020). Focal liver lesion detection in ultrasound image using deep feature fusions and super resolution. Machine Learning and Knowledge Extraction, 2(3), 10
  • Atalar, M. (2008). İmge Dizilerindeki Artıkların İşlenmesi. Gazi Üniversitesi Fen Bilimleri Enstitüsü Y. Lisans Tezi
  • Mittal, H., Saraswat, M., Bansal, J. C., & Nagar, A. (2020, December). Fake-face image classification using improved quantum-inspired evolutionary-based feature selection method. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 989-995). IEEE
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Enhancing Image Classification Performance through Discrete Cosine Transformation on Augmented Facial Images using GANs

Year 2023, Volume: IDAP-2023 : International Artificial Intelligence and Data Processing Symposium Issue: IDAP-2023, 7 - 18, 18.10.2023
https://doi.org/10.53070/bbd.1361811

Abstract

The continuous advancements in technology are profoundly influencing various domains, including the realm of artificial intelligence. Within this field, the development and training of facial recognition systems have emerged as one of the most prominent research areas. Nowadays, facial recognition systems are rapidly replacing traditional security methods. In order to develop a good face recognition system, the training process must be provided with sufficient data. Recently, the number of open-source data that can help improve the accuracy of face recognition systems is limited. Generative Adversarial Networks (GANs) are a type of machine learning algorithm comprising two interconnected neural networks that engage in a competitive relationship. It is widely used in work domains such as image creation, image manipulation, super-resolution, text visualization, photorealistic images, speech production, and face aging. In the study, the lack of data for training face recognition systems was first solved with synthetic face images obtained with GANs. In the subsequent stage of the investigation, the aim was to enhance the image classification procedure through the application of the discrete cosine transform to the images. This approach aimed to fortify facial recognition systems against the presence of authentic-looking fabricated faces within virtual environments. In the study, it was found that the classification of faces could be improved by 30% compared to the normal classification model. The primary objective of this research endeavor is to make a significant contribution towards the development of highly accurate facial recognition systems.

References

  • Wu, X., Xu, K., & Hall, P. (2017). A survey of image synthesis and editing with generative adversarial networks. Tsinghua Science and Technology, 22(6), 660-674
  • Bengio, Y., & LeCun, Y. (2007). Scaling learning algorithms towards AI. Large-scale kernel machines, 34(5), 1-41
  • François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018). An introduction to deep reinforcement learning. Foundations and Trends® in Machine Learning, 11(3-4), 219-354
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Advances in neural information processing systems. Curran Associates, Inc, 27, 2672-2680
  • Çelik, G., & Talu, M. F. (2020). Çekişmeli üretken ağ modellerinin görüntü üretme performanslarının incelenmesi. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(1), 181-192
  • Liu, Z., Qi, X., & Torr, P. H. (2020). Global texture enhancement for fake face detection in the wild. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8060-8069)
  • Wang, X., Guo, H., Hu, S., Chang, M. C., & Lyu, S. (2022). Gan-generated faces detection: A survey and new perspectives. arXiv preprint arXiv:2202.07145
  • Cho, J., Mirzaei, S., Oberg, J., & Kastner, R. (2009). Fpga-based face detection system using haar classifiers. In Proceedings of the ACM/SIGDA international symposium on Field programmable gate arrays (pp. 103-112)
  • Ayo, F. E., Mustapha, A. M., Braimah, J. A., & Aina, D. A. (2022). Geometric Analysis and YOLO Algorithm for Automatic Face Detection System in a Security Setting. In Journal of Physics: Conference Series (Vol. 2199, No. 1, p. 012010). IOP Publishing
  • Verma, A., Baljon, M., Mishra, S., Kaur, I., Saini, R., Saxena, S., & Sharma, S. K. (2022). Secure rotation invariant face detection system for authentication. CMC—Comput. Mater. Contin, 70, 1955-1974
  • Liao, Y., Tang, Z., Lei, J., Chen, J., & Tang, Z. (2022). Video Face Detection Technology and Its Application in Health Information Management System. Scientific Programming
  • Ullah, R., Hayat, H., Siddiqui, A. A., Siddiqui, U. A., Khan, J., Ullah, F., ... & Karami, G. M. (2022). A real-time framework for human face detection and recognition in cctv images. Mathematical Problems in Engineering
  • Obaida, T. H., Jamil, A. S., & Hassan, N. F. (2022). Real-time face detection in digital video-based on Viola-Jones supported by convolutional neural networks. International Journal of Electrical & Computer Engineering (2088-8708), 12(3)
  • Akgül, İ., & Funda, A. (2022). Derin Öğrenme Modeli ile Yüz İfadelerinden Duygu Tanıma. Journal of the Institute of Science and Technology, 12(1), 69-79
  • Tahir, F. S., Abdulrahman, A. A., & Hikmet Thanon, Z. (2022). Novel face detection algorithm with a mask on neural network training. International Journal of Nonlinear Analysis and Applications, 13(1), 209-215
  • Archana, M. C. P., Nitish, C. K., & Harikumar, S. (2022). Real time face detection and optimal face mapping for online classes. In Journal of Physics: Conference Series (Vol. 2161, No. 1, p. 012063). IOP Publishing
  • Liu, X., Deng, Z., & Yang, Y. (2019). Recent progress in semantic image segmentation. Artificial Intelligence Review, 52, 1089-1106
  • Campbell, J. B., & Wynne, R. H. (2011). Introduction to remote sensing. Guilford press
  • Gao J. (2009). Digital analysis of remotely sensed imagery. The Mc Graw-Hill Companies, USA
  • Karhan, Z., & Ergen, B. (2013, April). Classification of face images using discrete cosine transform. In 2013 21st Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE
  • Do, N. T., Na, I. S., & Kim, S. H. (2018). Forensics face detection from GANs using convolutional neural network. ISITC, 2018, 376-379
  • Atasoy, N. A., & Tabak, D. (2018). Destek Vektör Makineleri Kullanarak Yüz Tanima Uygulamasi Geliştirilmesi. Engineering Sciences, 13(2), 119-127
  • Öztürk, E., & Kurnaz, Ç. (2020). Görünüm Tabanlı Yüz Tanıma Yöntemleri Kullanılarak Cinsiyet Belirleme. Avrupa Bilim ve Teknoloji Dergisi, 111-120
  • Akbulut, Y., Şengür, A., & Ekici, S. (2017, September). Gender recognition from face images with deep learning. In 2017 International artificial intelligence and data processing symposium (IDAP) (pp. 1-4). IEEE
  • Adhinata, F., & Junaidi, A. (2022). Gender classification on video using FaceNet algorithm and supervised machine learning. International Journal of Computing and Digital Systems, 11(1), 199-208
  • Tao, X., & Pan, D. (2022). Face recognition based on scale invariant feature transform and fuzzy reasoning. Internet Technology Letters, e346
  • Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of big data, 6(1), 1-48
  • Alimovski, E. (2019). Derin öğrenmeye dayalı güçlü yüz tanıma sistemi için gan ile veri çoğaltma (Master's thesis, İstanbul Sabahattin Zaim Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği Anabilim Dalı)
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27
  • Bird, J. J., Barnes, C. M., Manso, L. J., Ekárt, A., & Faria, D. R. (2022). Fruit quality and defect image classification with conditional GAN data augmentation. Scientia Horticulturae, 293, 110684
  • C.I. a. P.Lab . Real and Fake Face Detection (ed.). Available: https://www.kaggle.com/ciplab/real-and-fake-facedetection (2019)
  • Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., ... & Chen, T. (2018). Recent advances in convolutional neural networks. Pattern recognition, 77, 354-377
  • Min, S., Lee, B., & Yoon, S. (2017). Deep learning in bioinformatics. Briefings in bioinformatics, 18(5), 851-869
  • Hanbay, K. (2020). Hyperspectral image classification using convolutional neural network and two-dimensional complex Gabor transform. Journal of the Faculty of Engineering and Architecture of Gazi University, 35(1), 443-456
  • Toğaçar, M., Ergen, B., & Özyurt, F. (2020). Evrişimsel sinir ağı modellerinde özellik seçim yöntemlerini kullanarak çiçek görüntülerinin sınıflandırılması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 32(1), 47-56.
  • Mateen, M., Wen, J., Nasrullah, Song, S., & Huang, Z. (2018). Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry, 11(1), 1
  • Mostafiz, R., Rahman, M. M., Islam, A. K., & Belkasim, S. (2020). Focal liver lesion detection in ultrasound image using deep feature fusions and super resolution. Machine Learning and Knowledge Extraction, 2(3), 10
  • Atalar, M. (2008). İmge Dizilerindeki Artıkların İşlenmesi. Gazi Üniversitesi Fen Bilimleri Enstitüsü Y. Lisans Tezi
  • Mittal, H., Saraswat, M., Bansal, J. C., & Nagar, A. (2020, December). Fake-face image classification using improved quantum-inspired evolutionary-based feature selection method. In 2020 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 989-995). IEEE
  • McCloskey, S., & Albright, M. (2018). Detecting gan-generated imagery using color cues. arXiv preprint arXiv:1812.08247
There are 40 citations in total.

Details

Primary Language English
Subjects Image Processing, Deep Learning, Computer Software
Journal Section PAPERS
Authors

Abdullah Şener 0000-0002-8927-5638

Burhan Ergen 0000-0003-3244-2615

Publication Date October 18, 2023
Submission Date September 17, 2023
Acceptance Date October 16, 2023
Published in Issue Year 2023 Volume: IDAP-2023 : International Artificial Intelligence and Data Processing Symposium Issue: IDAP-2023

Cite

APA Şener, A., & Ergen, B. (2023). Enhancing Image Classification Performance through Discrete Cosine Transformation on Augmented Facial Images using GANs. Computer Science, IDAP-2023 : International Artificial Intelligence and Data Processing Symposium(IDAP-2023), 7-18. https://doi.org/10.53070/bbd.1361811

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