Research Article
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Improving Image Classification Results by Applying Discrete Cosine Transform to Face Images

Year 2022, Volume: 27 Issue: 3, 1193 - 1206, 31.12.2022

Abdullah Şener , Burhan Ergen

https://doi.org/10.17482/uumfd.1076377

Abstract

Today, the development of technology enables the rapid development of artificial intelligence studies. One of the most popular topics among the developing artificial intelligence studies is the creation and use of realistic false faces in virtual environments. In the study, a series of studies were conducted to distinguish between false and real faces based on images containing false and real faces. In the study, two different classification models (VGG, Xception) and three different methods (normal image, Fourier transform image, discrete cosine transform image) were applied to images and separate classification processes were performed. The obtained results were compared and provided to the researchers as a resource.

Keywords

Image classification Deep learning Fourier transform Discrete Cosine transform

References

  • 1. 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.
  • 2. Akben, S. B., & Alkan, A. (2015). Density-based feature extraction to improve the classification performance in the datasets having low correlation between attributes. Journal of the Faculty of Engineering and Architecture of Gazi University, 30(4), 597-603.
  • 3. Akbulut, Y., Şengür, A., & Ekici, S. (2017). Gender recognition from face images with deep learning. In 2017 International artificial intelligence and data processing symposium (IDAP) (pp. 1-4).
  • 4. Atalar, M. (2008). İmge Dizilerindeki Artıkların İşlenmesi. Gazi Üniversitesi Fen Bilimleri Enstitüsü Y. Lisans Tezi.
  • 5. Atasoy, N. A., & Tabak, D. (2018). Destek Vektör Makineleri Kullanarak Yüz Tanima Uygulamasi Geliştirilmesi. Engineering Sciences, 13(2), 119-127.
  • 6. Bengio, Y., & LeCun, Y. (2007). Scaling learning algorithms towards AI. Large-scale kernel machines, 34(5), 1-41.
  • 7. C.I. a. P.Lab. (2019). Real and Fake Face Detection (ed.). Available: https://www.kaggle.com/ciplab/real-and-fake-facedetection
  • 8. Campbell, J. B., & Wynne, R. H. (2011). Introduction to remote sensing. Guilford Press.
  • 9. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251-1258).
  • 10. Cooley, J. W., & Tukey, J. W. (1965). An algorithm for the machine calculation of complex Fourier series. Mathematics of computation, 19(90), 297-301.
  • 11. Çelik, G., & Talu, M. F. (2019). Çekişmeli üretken ağ modellerinin görüntü üretme performanslarının incelenmesi. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(1), 181-192.
  • 12. Do, N. T., Na, I. S., & Kim, S. H. (2018). Forensics face detection from GANs using convolutional neural network. ISITC, 2018, 376-379.
  • 13. 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.
  • 14. Ganguly, S., Ganguly, A., Mohiuddin, S., Malakar, S., & Sarkar, R. (2022). ViXNet: Vision Transformer with Xception Network for deepfakes based video and image forgery detection. Expert Systems with Applications, 210, 118423.
  • 15. Gao, J. (2009). Digital analysis of remotely sensed imagery. McGraw-Hill Education.
  • 16. 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.
  • 17. 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.
  • 18. 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.
  • 19. Hanbay, K., Talu, M. F., & Özgüven, Ö. F. (2017). Fourıer Dönüşümü Kullanılarak Gerçek Zamanlı Kumaş Hatası Tespiti. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 32(1).
  • 20. Karhan, Z., & Ergen, B. (2013). Classification of face images using discrete cosine transform. In 2013 21st Signal Processing and Communications Applications Conference (SIU) (pp. 1-4).
  • 21. Kim, D., Choi, S., & Kwak, S. (2018). Deep learning based fake face detection. Journal of the Korea Industrial Information Systems Research, 23(5), 9-17.
  • 22. Kurt F., (2018). Makalenin başlığı. Sinir Ağlarında Hiper Parametrelerin Etkisinin İncelenmesi [yüksek lisans tezi]. Ankara:Hacettepe Üniversitesi.
  • 23. Liu, X., Deng, Z., & Yang, Y. (2019). Recent progress in semantic image segmentation. Artificial Intelligence Review, 52(2), 1089-1106.
  • 24. 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).
  • 25. M. Lin, Q. Chen, and S. Yan, (2014).“Network in network,” 2nd Int. Conf. Learn. Represent. ICLR 2014 - Conf. Track Proc.
  • 26. Mateen, M., Wen, J., Song, S., & Huang, Z. (2018). Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry, 11(1), 1.
  • 27. McCloskey, S., & Albright, M. (2019). Detecting GAN-generated imagery using saturation cues. In 2019 IEEE international conference on image processing (ICIP) (pp. 4584-4588).
  • 28. Min, S., Lee, B., & Yoon, S. (2017). Deep learning in bioinformatics. Briefings in bioinformatics, 18(5), 851-869.
  • 29. Mittal, H., Saraswat, M., Bansal, J. C., & Nagar, A. (2020). 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).
  • 30. 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.
  • 31. Niepert M. Ahmed M. Kutzkov K., (2014). Learning convolutional neural networks for graphs. In International conference on machine learning, . Germany:2016. p. 2014-2023.
  • 32. Öztürk, E., & Kurnaz, Ç. (2020). Görünüm Tabanlı Yüz Tanıma Yöntemleri Kullanılarak Cinsiyet Belirleme. Avrupa Bilim ve Teknoloji Dergisi, 111-120.
  • 33. Söylemez, Ö. F., & Ergen, B. (2020). Farklı Evrişimsel Sinir Ağı Mimarilerinin Yüz İfade Analizi Alanındaki Başarımlarının İncelenmesi. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 11(1), 123- 133.
  • 34. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).
  • 35. Tao, X., & Pan, D. (2022). Face Recognition based on Scale Invariant Feature Transform and Fuzzy Reasoning. Internet Technology Letters, e346.
  • 36. 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.
  • 37. Torun, T. K., & Marmarali, A. (2011). Online fault detection system for circular knitting machines. Textile and Apparel, 21(2), 164-170.
  • 38. 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.

YÜZ GÖRÜNTÜLERİNE AYRIK KOSİNÜS DÖNÜŞÜMÜ UYGULANARAK GÖRÜNTÜ SINIFLANDIRMA SONUÇLARININ İYİLEŞTİRİLMESİ

Year 2022, Volume: 27 Issue: 3, 1193 - 1206, 31.12.2022

Abdullah Şener , Burhan Ergen

https://doi.org/10.17482/uumfd.1076377

Abstract

Günümüzde teknolojinin gelişmesi yapay zekâ çalışmalarının da hızlı bir şekilde gelişmesine olarak sağlamaktadır. Gelişen yapay zekâ çalışmaları arasında son zamanlarda popülerliği yüksek olan konulardan birisi sanal ortamlarda gerçekçi sahte yüzlerin oluşturulması ve kullanılmasıdır. Yapılan çalışmada içerisinde sahte ve gerçek yüzlerin yer aldığı görüntüler kullanılarak yüzlerin sahte/gerçek olduğunu ayırt etmek için bir dizi çalışmalar yapılmıştır. Yapılan çalışmada iki farklı sınıflandırma modeli (VGG, Xception) ve görüntüler üzerinde üç faklı yöntem (normal görüntü, Fourier dönüşümlü görüntü, Ayrık Kosinüs dönüşümlü görüntü) uygulanarak ayrı ayrı sınıflandırma işlemleri gerçekleştirilmiştir. Elde edilen sonuçlar karşılaştırılarak araştırmacılara kaynak olarak sunulmuştur.

Keywords

Görüntü sınıflandırma Derin öğrenme Fourier dönüşümü Ayrık Kosinüs dönüşümü

References

  • 1. 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.
  • 2. Akben, S. B., & Alkan, A. (2015). Density-based feature extraction to improve the classification performance in the datasets having low correlation between attributes. Journal of the Faculty of Engineering and Architecture of Gazi University, 30(4), 597-603.
  • 3. Akbulut, Y., Şengür, A., & Ekici, S. (2017). Gender recognition from face images with deep learning. In 2017 International artificial intelligence and data processing symposium (IDAP) (pp. 1-4).
  • 4. Atalar, M. (2008). İmge Dizilerindeki Artıkların İşlenmesi. Gazi Üniversitesi Fen Bilimleri Enstitüsü Y. Lisans Tezi.
  • 5. Atasoy, N. A., & Tabak, D. (2018). Destek Vektör Makineleri Kullanarak Yüz Tanima Uygulamasi Geliştirilmesi. Engineering Sciences, 13(2), 119-127.
  • 6. Bengio, Y., & LeCun, Y. (2007). Scaling learning algorithms towards AI. Large-scale kernel machines, 34(5), 1-41.
  • 7. C.I. a. P.Lab. (2019). Real and Fake Face Detection (ed.). Available: https://www.kaggle.com/ciplab/real-and-fake-facedetection
  • 8. Campbell, J. B., & Wynne, R. H. (2011). Introduction to remote sensing. Guilford Press.
  • 9. Chollet, F. (2017). Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1251-1258).
  • 10. Cooley, J. W., & Tukey, J. W. (1965). An algorithm for the machine calculation of complex Fourier series. Mathematics of computation, 19(90), 297-301.
  • 11. Çelik, G., & Talu, M. F. (2019). Çekişmeli üretken ağ modellerinin görüntü üretme performanslarının incelenmesi. Balıkesir Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 22(1), 181-192.
  • 12. Do, N. T., Na, I. S., & Kim, S. H. (2018). Forensics face detection from GANs using convolutional neural network. ISITC, 2018, 376-379.
  • 13. 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.
  • 14. Ganguly, S., Ganguly, A., Mohiuddin, S., Malakar, S., & Sarkar, R. (2022). ViXNet: Vision Transformer with Xception Network for deepfakes based video and image forgery detection. Expert Systems with Applications, 210, 118423.
  • 15. Gao, J. (2009). Digital analysis of remotely sensed imagery. McGraw-Hill Education.
  • 16. 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.
  • 17. 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.
  • 18. 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.
  • 19. Hanbay, K., Talu, M. F., & Özgüven, Ö. F. (2017). Fourıer Dönüşümü Kullanılarak Gerçek Zamanlı Kumaş Hatası Tespiti. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 32(1).
  • 20. Karhan, Z., & Ergen, B. (2013). Classification of face images using discrete cosine transform. In 2013 21st Signal Processing and Communications Applications Conference (SIU) (pp. 1-4).
  • 21. Kim, D., Choi, S., & Kwak, S. (2018). Deep learning based fake face detection. Journal of the Korea Industrial Information Systems Research, 23(5), 9-17.
  • 22. Kurt F., (2018). Makalenin başlığı. Sinir Ağlarında Hiper Parametrelerin Etkisinin İncelenmesi [yüksek lisans tezi]. Ankara:Hacettepe Üniversitesi.
  • 23. Liu, X., Deng, Z., & Yang, Y. (2019). Recent progress in semantic image segmentation. Artificial Intelligence Review, 52(2), 1089-1106.
  • 24. 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).
  • 25. M. Lin, Q. Chen, and S. Yan, (2014).“Network in network,” 2nd Int. Conf. Learn. Represent. ICLR 2014 - Conf. Track Proc.
  • 26. Mateen, M., Wen, J., Song, S., & Huang, Z. (2018). Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry, 11(1), 1.
  • 27. McCloskey, S., & Albright, M. (2019). Detecting GAN-generated imagery using saturation cues. In 2019 IEEE international conference on image processing (ICIP) (pp. 4584-4588).
  • 28. Min, S., Lee, B., & Yoon, S. (2017). Deep learning in bioinformatics. Briefings in bioinformatics, 18(5), 851-869.
  • 29. Mittal, H., Saraswat, M., Bansal, J. C., & Nagar, A. (2020). 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).
  • 30. 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.
  • 31. Niepert M. Ahmed M. Kutzkov K., (2014). Learning convolutional neural networks for graphs. In International conference on machine learning, . Germany:2016. p. 2014-2023.
  • 32. Öztürk, E., & Kurnaz, Ç. (2020). Görünüm Tabanlı Yüz Tanıma Yöntemleri Kullanılarak Cinsiyet Belirleme. Avrupa Bilim ve Teknoloji Dergisi, 111-120.
  • 33. Söylemez, Ö. F., & Ergen, B. (2020). Farklı Evrişimsel Sinir Ağı Mimarilerinin Yüz İfade Analizi Alanındaki Başarımlarının İncelenmesi. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 11(1), 123- 133.
  • 34. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2818-2826).
  • 35. Tao, X., & Pan, D. (2022). Face Recognition based on Scale Invariant Feature Transform and Fuzzy Reasoning. Internet Technology Letters, e346.
  • 36. 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.
  • 37. Torun, T. K., & Marmarali, A. (2011). Online fault detection system for circular knitting machines. Textile and Apparel, 21(2), 164-170.
  • 38. 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.
There are 38 citations in total.

Details

Primary Language Turkish
Subjects Artificial Intelligence
Journal Section Research Articles
Authors

Abdullah Şener 0000-0002-8927-5638

Burhan Ergen 0000-0003-3244-2615

Early Pub Date December 9, 2022
Publication Date December 31, 2022
Submission Date February 20, 2022
Acceptance Date December 6, 2022
Published in Issue Year 2022 Volume: 27 Issue: 3

Cite

APA Şener, A., & Ergen, B. (2022). YÜZ GÖRÜNTÜLERİNE AYRIK KOSİNÜS DÖNÜŞÜMÜ UYGULANARAK GÖRÜNTÜ SINIFLANDIRMA SONUÇLARININ İYİLEŞTİRİLMESİ. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 27(3), 1193-1206. https://doi.org/10.17482/uumfd.1076377
AMA Şener A, Ergen B. YÜZ GÖRÜNTÜLERİNE AYRIK KOSİNÜS DÖNÜŞÜMÜ UYGULANARAK GÖRÜNTÜ SINIFLANDIRMA SONUÇLARININ İYİLEŞTİRİLMESİ. UUJFE. December 2022;27(3):1193-1206. doi:10.17482/uumfd.1076377
Chicago Şener, Abdullah, and Burhan Ergen. “YÜZ GÖRÜNTÜLERİNE AYRIK KOSİNÜS DÖNÜŞÜMÜ UYGULANARAK GÖRÜNTÜ SINIFLANDIRMA SONUÇLARININ İYİLEŞTİRİLMESİ”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 27, no. 3 (December 2022): 1193-1206. https://doi.org/10.17482/uumfd.1076377.
EndNote Şener A, Ergen B (December 1, 2022) YÜZ GÖRÜNTÜLERİNE AYRIK KOSİNÜS DÖNÜŞÜMÜ UYGULANARAK GÖRÜNTÜ SINIFLANDIRMA SONUÇLARININ İYİLEŞTİRİLMESİ. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 27 3 1193–1206.
IEEE A. Şener and B. Ergen, “YÜZ GÖRÜNTÜLERİNE AYRIK KOSİNÜS DÖNÜŞÜMÜ UYGULANARAK GÖRÜNTÜ SINIFLANDIRMA SONUÇLARININ İYİLEŞTİRİLMESİ”, UUJFE, vol. 27, no. 3, pp. 1193–1206, 2022, doi: 10.17482/uumfd.1076377.
ISNAD Şener, Abdullah - Ergen, Burhan. “YÜZ GÖRÜNTÜLERİNE AYRIK KOSİNÜS DÖNÜŞÜMÜ UYGULANARAK GÖRÜNTÜ SINIFLANDIRMA SONUÇLARININ İYİLEŞTİRİLMESİ”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 27/3 (December 2022), 1193-1206. https://doi.org/10.17482/uumfd.1076377.
JAMA Şener A, Ergen B. YÜZ GÖRÜNTÜLERİNE AYRIK KOSİNÜS DÖNÜŞÜMÜ UYGULANARAK GÖRÜNTÜ SINIFLANDIRMA SONUÇLARININ İYİLEŞTİRİLMESİ. UUJFE. 2022;27:1193–1206.
MLA Şener, Abdullah and Burhan Ergen. “YÜZ GÖRÜNTÜLERİNE AYRIK KOSİNÜS DÖNÜŞÜMÜ UYGULANARAK GÖRÜNTÜ SINIFLANDIRMA SONUÇLARININ İYİLEŞTİRİLMESİ”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol. 27, no. 3, 2022, pp. 1193-06, doi:10.17482/uumfd.1076377.
Vancouver Şener A, Ergen B. YÜZ GÖRÜNTÜLERİNE AYRIK KOSİNÜS DÖNÜŞÜMÜ UYGULANARAK GÖRÜNTÜ SINIFLANDIRMA SONUÇLARININ İYİLEŞTİRİLMESİ. UUJFE. 2022;27(3):1193-206.

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