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Facial Tracking, Recognition, And Utilizing Gaussian Blur In Face Recognition Sytems Via The OpenCv Library

Yıl 2024, Cilt: 8 Sayı: 2, 103 - 122, 30.12.2024
https://doi.org/10.47897/bilmes.1501078

Öz

Face recognition technology attracts great attention in many technological areas. The development of face recognition algorithms has made significant contributions to the elimination of deficiencies in the field of image processing. Especially image processing libraries such as OpenCV provide a reliable and regularly updated platform for researchers and developers. OpenCv, which includes face recognition algorithms, is an image processing library that facilitates image processing. Some people may not want their faces to be seen in videos, movies or live broadcasts, and objectionable images and harmful products such as cigarettes and alcohol may need to be censored. In this case, the Gaussian filter comes to our rescue. The Gaussian filter is a filter widely used in image processing techniques and known for its blurring feature. The Gaussian filter is also called blurring in image processing software. The Python language is a programming language that can work independently of the platform. The Python language contains many libraries and is easy to program. The OpenCv library, like many other libraries, has generally been used with the Python language because it works very well with the Python language and is easily programmed. Many projects developed with Python language and OpenCv can be seen in academic sources. The aim of this study is to perform face recognition using OpenCV library and automatically apply Gaussian filter to recognized faces. All existing software does not automatically blur the desired faces. Doing this process manually is both time-consuming and jeopardizes the protection of privacy due to the unnoticed parts of the manual application process. Possible users of this project include televisions, production companies, broadcasters and YouTubers. This project can contribute to more effective protection of privacy and save time. This article can provide a method for researchers, industry experts and academics.

Kaynakça

  • [1] C. M. Bishop, Pattern recognition and machine learning. Springer, 2006.
  • [2] G. Bradski, and A. Kaehler, Learning OpenCV: Computer vision with the OpenCV library. O'Reilly Media, Inc, 2008.
  • [3] R. C. Gonzalez, and R. E. Woods, Digital image processing. Prentice Hall, 2008.
  • [4] R. C. Gonzalez, and R. E. Woods, Digital image processing (4th ed.). Pearson, 2018.
  • [5] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
  • [6] A. Jain, Fundamentals of digital image processing. Prentice-Hall. Press, 1989.
  • [7] A. Jain and S. Li, Handbook of Face Recognition. Springer, 2011.
  • [8] A. K. Jain, R. Kasturi and B. G. Schunck, Machine vision. McGraw-Hill, 1995.
  • [9] V. M. Patel and R. Chellappa, Sparse representations and compressive sensing for imaging and vision. Springer, 2014.
  • [10] S. J. D. Prince, Computer vision: Models, learning, and inference. Cambridge University Press, 2012.
  • [11] J. C. Russ, The image processing handbook. CRC press, 2011.
  • [12] Datagen. Facial recognition algorithms and libraries you should know, 2022. [Online]. Available: https://www.datagen.tech/blog/facial-recognition-algorithms-and-libraries-you-should-know.
  • [13] R. Szeliski, Computer vision: Algorithms and applications. Springer, 2010.
  • [14] S. Kulkarni, Understanding image filtering techniques in image processing, 2023.. [Online]. Available: https://www.imageprovision.com/articles/understanding-image-filtering-techniques-in-image-processing.
  • [15] T. Ahonen, A. Hadid, and M. Pietikäinen, Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, 2006. [Abstract]. Available: ProQuest, https://www.scirp.org/reference/referencespapers?referenceid=2037884.
  • [16] P. J. Burt and E. H. Adelson, The Laplacian Pyramid as a Compact Image Code. IEEE Transactions on Communications, vol. 31, no. 4, pp. 532-540, 1983. https://doi.org/10.1109/TCOM.1983.1095851.
  • [17] J. Azmeen, and D. J. Borah, (2021). Face recognition techniques and challenges: A review. In N. Marriwala, C. C. Tripathi, S. Jain, & S. Mathapathi (Eds.), Soft computing for intelligent systems (pp. 345-360). Springer, 2021. https://doi.org/10.1007/978-981-16-1048-6_27
  • [18] A. Bhat, R. K. Jha, and V. Kedia, Robust face detection and recognition using image processing and OpenCV. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC). IEEE. , 2022. https://doi.org/10.1109/ICCMC53470.2022.9753792
  • [19] V. Bhavani, K. S. Priya, A. K. Sirivarshitha, and K. Sravani, An approach for face detection and face recognition using OpenCV and face recognition libraries in Python. In 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE., 2023. https://doi.org/10.1109/ICACCS57279.2023.10113066
  • [20] L. M. Gladence, M. M. Khan, S. Mohammad, et al. 2022 6th International Conference on Computing Methodologies and Communication (ICCMC). IEEE., 2022. https://doi.org/10.1109/ICCMC53470.2022
  • [21] N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar, Attribute and Simile Classifiers for Face Verification. Proceedings of the IEEE International Conference on Computer Vision, 2009.
  • [22] R. Lienhart, and J. Maydt, An Extended Set of Haar-like Features for Rapid Object Detection. Proceedings of the IEEE International Conference on Image Processing. IEEE, 2002.
  • [23] C. Liu, and H. Wechsler, Enhanced Fisher Linear Discriminant Models for Face Recognition. Proceedings of the IEEE International Conference on Image Processing. IEEE, 2003.
  • [24] Masi, I., Tran, A. T., Hassner, T., and Medioni, G. Pose-aware face recognition in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2018.
  • [25] O. M. Parkhi, A. Vedaldi and A. Zisserman, Deep face recognition. British Machine Vision Conference, 2015.
  • [26] S. Ren, K. He, R.Girshick and J. Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems, 2015.
  • [27] P. Viola and M. J. Jones, Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), 1, I-511-I-518, 2001. https://doi.org/10.1109/CVPR.2001.990517
  • [28] I. Bamba, J. Yashika, Singh and P. Chawla, Face recognition techniques and implementation. Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India, 2022.
  • [29] M. Ganvir, A. Panchabhai, N. Sakhare, R. Thelkar and K. Wani, Face recognition using OpenCV. Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur, Maharashtra, India, 2023.
  • [30] P. J. Phillips, et al, The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1090-1104, 2000.
  • [31] W.Zhao, R. Chellappa, A. Rosenfeld and P. J. Phillips, Face recognition: A literature survey. ACM Computing Surveys, vol. 35, no. 4, pp. 399-458, 2003. https://doi.org/10.1145/954339.954342
  • [32] N. Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series. MIT Press, 1949
  • [33] C. Tomasi and R. Manduchi, Bilateral Filtering for Gray and Color Images. Proceedings of IEEE Conference on Computer Vision. IEEE, 1998. https://doi.org/10.1109/ICCV.1998.710815
  • [34] I. Sobel, An Isotropic 3x3 Image Gradient Operator. HPLABS, Measurement and Manufacturing Research Center. [Online]. Available: https://researchgate.net/publication/239398674_An_Isotropic_3x3_Image_Gradient_Operator.
  • [35] H. Bay, T. Tuytelaars, and L. Van Gol, SURF: Speeded Up Robust Features. European Conference on Computer Vision, 2006.
  • [36] D.G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, vol. 60, no. 2, 2004.
  • [37] Y. Taigman, M. Yang, and L. Wolf, DeepFace: Closing the Gap to Human-Level Performance in Face Verification. IEEE Conference on Computer Vision and Pattern Recognition, 2014.
  • [38] N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005.
  • [39] D. Aydemir, Revitalizing Turkish Mythological Elements through Artificial Intelligence Applications in Graphic Design: A Case Study on Midjourney, ISVOS, vol. 7, no. 2, pp. 187–205, 2023, https://doi.org/10.47897/bilmes.1400144.
  • [40] U. Saray and U. Çavdar, “Comparison of Different Optimization Algorithms in the Fashion MNIST Dataset”, IJMSIT, vol. 8, no. 2, pp. 52–58, 2024.
  • [41] V. Karaca and E. Yaşar, “Performing Distance Measurements Of Fixed Objects Detected With Yolo Using Web Camera”, ISVOS, c. 8, sy. 1, ss. 87–93, 2024, https://doi.org/10.47897/bilmes.1502873.
  • [42] F. Salahshoor (Director), Prophet Joseph [TV series]. IRIB, 2008.
  • [43] M.Sonka, V. Hlavac and R. Boyle, Image processing, analysis, and machine vision. Cengage Learning, 2014.
  • [44] Itseez. Open Source Computer Vision Library, 2015. [Online]. Available: https://opencv.org/.
  • [45] K. Fukunaga and L. D. Hostetler, The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition. IEEE Transactions on Information Theory, vol. 21, no. 1, pp. 32-40, 1975. [Abstract]. Available: ProQuest, https://www.scirp.org/reference/referencespapers?referenceid=2651561.
  • [46] R. Lini, Different filters for image processing, 2021. [Online]. Available: https://medium.com/@rajilini/different-filters-for-image-processing-698e72924101.
  • [47] G. Bradski, The OpenCV Library. Dr. Dobb's Journal of Software Tools, 2000.
  • [48] D. Comaniciu and P. Meer, Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, 2002. https://doi.org/10.1109/34.1000236.
  • [49] A. Majumdar and R. K. Ward, Robust Classifiers for Data Reduced via Random Projections. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 40, no. 5, pp. 1359-1371, 2010. https://doi.org/10.1109/TSMCB.2009.2038493.
  • [50] P. Turaga, R. Chellappa, V. S. Subrahmanian and O. Udrea, Machine recognition of human activities: A survey. IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 11, pp. 1473-1488, 2008. [Abstract]. Available: ProQuest, https://ieeexplore.ieee.org/document/4633644.
  • [51] G. Özmen and R. Kandemir, Haar Dalgacıkları ve Kübik Bezier Eğrileri İle Yüz İfadesi Tespiti. ELECO '2012 Elektrik - Elektronik ve Bilgisayar Mühendisliği Sempozyumu, 2012.
  • [52] K. Kadir, M. K. Kamaruddin, H. Nasir, S. Safie, Z. Bakti, A comparative study between LBP and Haar-like features for Face Detection using OpenCV. ICE2T 4th International Conference on Engineering Technology and Technopreneuship, 2014.

Facial Tracking, Recognition, and Utilizing Gaussian Blur In Face Recognition Sytems Via The OpenCv Library

Yıl 2024, Cilt: 8 Sayı: 2, 103 - 122, 30.12.2024
https://doi.org/10.47897/bilmes.1501078

Öz

Face recognition technology attracts great attention in many technological areas. The development of face recognition algorithms has made significant contributions to the elimination of deficiencies in the field of image processing. Especially image processing libraries such as OpenCV provide a reliable and regularly updated platform for researchers and developers. OpenCv, which includes face recognition algorithms, is an image processing library that facilitates image processing. Some people may not want their faces to be seen in videos, movies or live broadcasts, and objectionable images and harmful products such as cigarettes and alcohol may need to be censored. In this case, the Gaussian filter comes to our rescue. The Gaussian filter is a filter widely used in image processing techniques and known for its blurring feature. The Gaussian filter is also called blurring in image processing software. The Python language is a programming language that can work independently of the platform. The Python language contains many libraries and is easy to program. The OpenCv library, like many other libraries, has generally been used with the Python language because it works very well with the Python language and is easily programmed. Many projects developed with Python language and OpenCv can be seen in academic sources. The aim of this study is to perform face recognition using OpenCV library and automatically apply Gaussian filter to recognized faces. All existing software does not automatically blur the desired faces. Doing this process manually is both time-consuming and jeopardizes the protection of privacy due to the unnoticed parts of the manual application process. Possible users of this project include televisions, production companies, broadcasters and YouTubers. This project can contribute to more effective protection of privacy and save time. This article can provide a method for researchers, industry experts and academics.

Kaynakça

  • [1] C. M. Bishop, Pattern recognition and machine learning. Springer, 2006.
  • [2] G. Bradski, and A. Kaehler, Learning OpenCV: Computer vision with the OpenCV library. O'Reilly Media, Inc, 2008.
  • [3] R. C. Gonzalez, and R. E. Woods, Digital image processing. Prentice Hall, 2008.
  • [4] R. C. Gonzalez, and R. E. Woods, Digital image processing (4th ed.). Pearson, 2018.
  • [5] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
  • [6] A. Jain, Fundamentals of digital image processing. Prentice-Hall. Press, 1989.
  • [7] A. Jain and S. Li, Handbook of Face Recognition. Springer, 2011.
  • [8] A. K. Jain, R. Kasturi and B. G. Schunck, Machine vision. McGraw-Hill, 1995.
  • [9] V. M. Patel and R. Chellappa, Sparse representations and compressive sensing for imaging and vision. Springer, 2014.
  • [10] S. J. D. Prince, Computer vision: Models, learning, and inference. Cambridge University Press, 2012.
  • [11] J. C. Russ, The image processing handbook. CRC press, 2011.
  • [12] Datagen. Facial recognition algorithms and libraries you should know, 2022. [Online]. Available: https://www.datagen.tech/blog/facial-recognition-algorithms-and-libraries-you-should-know.
  • [13] R. Szeliski, Computer vision: Algorithms and applications. Springer, 2010.
  • [14] S. Kulkarni, Understanding image filtering techniques in image processing, 2023.. [Online]. Available: https://www.imageprovision.com/articles/understanding-image-filtering-techniques-in-image-processing.
  • [15] T. Ahonen, A. Hadid, and M. Pietikäinen, Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 12, pp. 2037-2041, 2006. [Abstract]. Available: ProQuest, https://www.scirp.org/reference/referencespapers?referenceid=2037884.
  • [16] P. J. Burt and E. H. Adelson, The Laplacian Pyramid as a Compact Image Code. IEEE Transactions on Communications, vol. 31, no. 4, pp. 532-540, 1983. https://doi.org/10.1109/TCOM.1983.1095851.
  • [17] J. Azmeen, and D. J. Borah, (2021). Face recognition techniques and challenges: A review. In N. Marriwala, C. C. Tripathi, S. Jain, & S. Mathapathi (Eds.), Soft computing for intelligent systems (pp. 345-360). Springer, 2021. https://doi.org/10.1007/978-981-16-1048-6_27
  • [18] A. Bhat, R. K. Jha, and V. Kedia, Robust face detection and recognition using image processing and OpenCV. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC). IEEE. , 2022. https://doi.org/10.1109/ICCMC53470.2022.9753792
  • [19] V. Bhavani, K. S. Priya, A. K. Sirivarshitha, and K. Sravani, An approach for face detection and face recognition using OpenCV and face recognition libraries in Python. In 2023 9th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE., 2023. https://doi.org/10.1109/ICACCS57279.2023.10113066
  • [20] L. M. Gladence, M. M. Khan, S. Mohammad, et al. 2022 6th International Conference on Computing Methodologies and Communication (ICCMC). IEEE., 2022. https://doi.org/10.1109/ICCMC53470.2022
  • [21] N. Kumar, A. C. Berg, P. N. Belhumeur, and S. K. Nayar, Attribute and Simile Classifiers for Face Verification. Proceedings of the IEEE International Conference on Computer Vision, 2009.
  • [22] R. Lienhart, and J. Maydt, An Extended Set of Haar-like Features for Rapid Object Detection. Proceedings of the IEEE International Conference on Image Processing. IEEE, 2002.
  • [23] C. Liu, and H. Wechsler, Enhanced Fisher Linear Discriminant Models for Face Recognition. Proceedings of the IEEE International Conference on Image Processing. IEEE, 2003.
  • [24] Masi, I., Tran, A. T., Hassner, T., and Medioni, G. Pose-aware face recognition in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2018.
  • [25] O. M. Parkhi, A. Vedaldi and A. Zisserman, Deep face recognition. British Machine Vision Conference, 2015.
  • [26] S. Ren, K. He, R.Girshick and J. Sun, Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. Advances in Neural Information Processing Systems, 2015.
  • [27] P. Viola and M. J. Jones, Rapid object detection using a boosted cascade of simple features. Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), 1, I-511-I-518, 2001. https://doi.org/10.1109/CVPR.2001.990517
  • [28] I. Bamba, J. Yashika, Singh and P. Chawla, Face recognition techniques and implementation. Manav Rachna International Institute of Research and Studies, Faridabad, Haryana, India, 2022.
  • [29] M. Ganvir, A. Panchabhai, N. Sakhare, R. Thelkar and K. Wani, Face recognition using OpenCV. Rashtrasant Tukadoji Maharaj Nagpur University, Nagpur, Maharashtra, India, 2023.
  • [30] P. J. Phillips, et al, The FERET evaluation methodology for face-recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 10, pp. 1090-1104, 2000.
  • [31] W.Zhao, R. Chellappa, A. Rosenfeld and P. J. Phillips, Face recognition: A literature survey. ACM Computing Surveys, vol. 35, no. 4, pp. 399-458, 2003. https://doi.org/10.1145/954339.954342
  • [32] N. Wiener, Extrapolation, Interpolation, and Smoothing of Stationary Time Series. MIT Press, 1949
  • [33] C. Tomasi and R. Manduchi, Bilateral Filtering for Gray and Color Images. Proceedings of IEEE Conference on Computer Vision. IEEE, 1998. https://doi.org/10.1109/ICCV.1998.710815
  • [34] I. Sobel, An Isotropic 3x3 Image Gradient Operator. HPLABS, Measurement and Manufacturing Research Center. [Online]. Available: https://researchgate.net/publication/239398674_An_Isotropic_3x3_Image_Gradient_Operator.
  • [35] H. Bay, T. Tuytelaars, and L. Van Gol, SURF: Speeded Up Robust Features. European Conference on Computer Vision, 2006.
  • [36] D.G. Lowe, Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision, vol. 60, no. 2, 2004.
  • [37] Y. Taigman, M. Yang, and L. Wolf, DeepFace: Closing the Gap to Human-Level Performance in Face Verification. IEEE Conference on Computer Vision and Pattern Recognition, 2014.
  • [38] N. Dalal and B. Triggs, Histograms of Oriented Gradients for Human Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2005.
  • [39] D. Aydemir, Revitalizing Turkish Mythological Elements through Artificial Intelligence Applications in Graphic Design: A Case Study on Midjourney, ISVOS, vol. 7, no. 2, pp. 187–205, 2023, https://doi.org/10.47897/bilmes.1400144.
  • [40] U. Saray and U. Çavdar, “Comparison of Different Optimization Algorithms in the Fashion MNIST Dataset”, IJMSIT, vol. 8, no. 2, pp. 52–58, 2024.
  • [41] V. Karaca and E. Yaşar, “Performing Distance Measurements Of Fixed Objects Detected With Yolo Using Web Camera”, ISVOS, c. 8, sy. 1, ss. 87–93, 2024, https://doi.org/10.47897/bilmes.1502873.
  • [42] F. Salahshoor (Director), Prophet Joseph [TV series]. IRIB, 2008.
  • [43] M.Sonka, V. Hlavac and R. Boyle, Image processing, analysis, and machine vision. Cengage Learning, 2014.
  • [44] Itseez. Open Source Computer Vision Library, 2015. [Online]. Available: https://opencv.org/.
  • [45] K. Fukunaga and L. D. Hostetler, The Estimation of the Gradient of a Density Function, with Applications in Pattern Recognition. IEEE Transactions on Information Theory, vol. 21, no. 1, pp. 32-40, 1975. [Abstract]. Available: ProQuest, https://www.scirp.org/reference/referencespapers?referenceid=2651561.
  • [46] R. Lini, Different filters for image processing, 2021. [Online]. Available: https://medium.com/@rajilini/different-filters-for-image-processing-698e72924101.
  • [47] G. Bradski, The OpenCV Library. Dr. Dobb's Journal of Software Tools, 2000.
  • [48] D. Comaniciu and P. Meer, Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 5, pp. 603-619, 2002. https://doi.org/10.1109/34.1000236.
  • [49] A. Majumdar and R. K. Ward, Robust Classifiers for Data Reduced via Random Projections. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 40, no. 5, pp. 1359-1371, 2010. https://doi.org/10.1109/TSMCB.2009.2038493.
  • [50] P. Turaga, R. Chellappa, V. S. Subrahmanian and O. Udrea, Machine recognition of human activities: A survey. IEEE Transactions on Circuits and Systems for Video Technology, vol. 18, no. 11, pp. 1473-1488, 2008. [Abstract]. Available: ProQuest, https://ieeexplore.ieee.org/document/4633644.
  • [51] G. Özmen and R. Kandemir, Haar Dalgacıkları ve Kübik Bezier Eğrileri İle Yüz İfadesi Tespiti. ELECO '2012 Elektrik - Elektronik ve Bilgisayar Mühendisliği Sempozyumu, 2012.
  • [52] K. Kadir, M. K. Kamaruddin, H. Nasir, S. Safie, Z. Bakti, A comparative study between LBP and Haar-like features for Face Detection using OpenCV. ICE2T 4th International Conference on Engineering Technology and Technopreneuship, 2014.
Toplam 52 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Görüntü İşleme, Yapay Zeka (Diğer)
Bölüm Makaleler
Yazarlar

Muhammed Emin Necipsoy 0000-0003-3978-6365

Atilla Ergüzen 0000-0003-4562-2578

Erken Görünüm Tarihi 30 Aralık 2024
Yayımlanma Tarihi 30 Aralık 2024
Gönderilme Tarihi 24 Haziran 2024
Kabul Tarihi 27 Ağustos 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 8 Sayı: 2

Kaynak Göster

APA Necipsoy, M. E., & Ergüzen, A. (2024). Facial Tracking, Recognition, And Utilizing Gaussian Blur In Face Recognition Sytems Via The OpenCv Library. International Scientific and Vocational Studies Journal, 8(2), 103-122. https://doi.org/10.47897/bilmes.1501078
AMA Necipsoy ME, Ergüzen A. Facial Tracking, Recognition, And Utilizing Gaussian Blur In Face Recognition Sytems Via The OpenCv Library. ISVOS. Aralık 2024;8(2):103-122. doi:10.47897/bilmes.1501078
Chicago Necipsoy, Muhammed Emin, ve Atilla Ergüzen. “Facial Tracking, Recognition, And Utilizing Gaussian Blur In Face Recognition Sytems Via The OpenCv Library”. International Scientific and Vocational Studies Journal 8, sy. 2 (Aralık 2024): 103-22. https://doi.org/10.47897/bilmes.1501078.
EndNote Necipsoy ME, Ergüzen A (01 Aralık 2024) Facial Tracking, Recognition, And Utilizing Gaussian Blur In Face Recognition Sytems Via The OpenCv Library. International Scientific and Vocational Studies Journal 8 2 103–122.
IEEE M. E. Necipsoy ve A. Ergüzen, “Facial Tracking, Recognition, And Utilizing Gaussian Blur In Face Recognition Sytems Via The OpenCv Library”, ISVOS, c. 8, sy. 2, ss. 103–122, 2024, doi: 10.47897/bilmes.1501078.
ISNAD Necipsoy, Muhammed Emin - Ergüzen, Atilla. “Facial Tracking, Recognition, And Utilizing Gaussian Blur In Face Recognition Sytems Via The OpenCv Library”. International Scientific and Vocational Studies Journal 8/2 (Aralık 2024), 103-122. https://doi.org/10.47897/bilmes.1501078.
JAMA Necipsoy ME, Ergüzen A. Facial Tracking, Recognition, And Utilizing Gaussian Blur In Face Recognition Sytems Via The OpenCv Library. ISVOS. 2024;8:103–122.
MLA Necipsoy, Muhammed Emin ve Atilla Ergüzen. “Facial Tracking, Recognition, And Utilizing Gaussian Blur In Face Recognition Sytems Via The OpenCv Library”. International Scientific and Vocational Studies Journal, c. 8, sy. 2, 2024, ss. 103-22, doi:10.47897/bilmes.1501078.
Vancouver Necipsoy ME, Ergüzen A. Facial Tracking, Recognition, And Utilizing Gaussian Blur In Face Recognition Sytems Via The OpenCv Library. ISVOS. 2024;8(2):103-22.


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