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Year 2023, Volume: 1 Issue: 2, 93 - 103, 02.02.2024

Muhammed Taha Aydın Oğuzhan Menemencioğlu İlhami Muharrem Orak

Abstract

References

  • [1] B. Girod and D. I. Processing, “Image Processing and Related Fields,” no. 22, [Online]. Available: http://fourier.eng.hmc.edu/e161/lectures/e161ch1.pdf
  • [2] T. Prabaharan, P. Periasamy, V. Mugendiran, and Ramanan, “Studies on application of image processing in various fields: An overview,” IOP Conf Ser Mater Sci Eng, vol. 961, p. 012006, Nov. 2020, doi: 10.1088/1757-899X/961/1/012006.
  • [3] W. Dhifli and A. B. Diallo, “Face Recognition in the Wild,” Procedia Comput Sci, vol. 96, pp. 1571–1580, 2016, doi: 10.1016/j.procs.2016.08.204.
  • [4] Y. Barlas, “Havalimanına da ‘yüz tanıma’ geliyor.,” Gazete Habertürk, 2016.
  • [5] N. Singla, G. Singla, and N. Maggon, “Face Detection and Recognition Using Digital Image Processing: ‘State of the Art,’” in 2022 2nd International Conference on Innovative Sustainable Computational Technologies (CISCT), IEEE, Dec. 2022, pp. 1–4. doi: 10.1109/CISCT55310.2022.10046441.
  • [6] Ösym, “Ösym Yüz Tanıma,” Ösym. [Online]. Available: http://www.osym.gov.tr/TR,12816/osymden-elektroniksinavlarda-yuz-tanima-donemi-30112016.html
  • [7] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 886–893, 2005, doi: 10.1109/CVPR.2005.177.
  • [8] F. Karakaya, H. Altun, and A. Çavuşlu, “Gerçek Zamanlı Nesne Tanıma Uygulamaları için HOG Algoritmasının FPGA Tabanlı Gömülü Sistem Uyarlaması Implementation of HOG algorithm for Real Time Object Recognition Applications on FPGA based Embedded System”.
  • [9] P. Viola and M. 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, vol. 1, pp. I-511-I–518, 2001, doi: 10.1109/CVPR.2001.990517.
  • [10] M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586–591, 1991, doi: 10.1109/CVPR.1991.139758.
  • [11] H. Sultan, M. H. Zafar, S. Anwer, A. Waris, H. Ijaz, and M. Sarwar, “Real Time Face Recognition Based Attendance System For University Classroom,” 2nd IEEE International Conference on Artificial Intelligence, ICAI 2022, no. March 2021, pp. 165–168, 2022, doi: 10.1109/ICAI55435.2022.9773650.
  • [12] K.Kanimozhi, “Biometric Authentication for Online Examination,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 3, no. 9, 2015, [Online]. Available: https://www.ijircce.com/upload/2015/september/149_Biometric.pdf
  • [13] A. Fayyoumi and A. Zarrad, “Novel Solution Based on Face Recognition to Address Identity Theft and Cheating in Online Examination Systems,” Scientific Research, 2014.
  • [14] A. Raghuwanshi and P. D. Swami, “An automated classroom attendance system using video based face recognition,” in 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE, May 2017, pp. 719–724. doi: 10.1109/RTEICT.2017.8256691.
  • [15] A. R. S. Siswanto, A. S. Nugroho, and M. Galinium, “Implementation of face recognition algorithm for biometrics based time attendance system,” Proceedings - 2014 International Conference on ICT for Smart Society: “Smart System Platform Development for City and Society, GoeSmart 2014”, ICISS 2014, pp. 149–154, 2014, doi: 10.1109/ICTSS.2014.7013165.
  • [16] J. Hu, J. Lu, and Y.-P. Tan, “Discriminative Deep Metric Learning for Face Verification in the Wild,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Jun. 2014, pp. 1875–1882. doi: 10.1109/CVPR.2014.242.
  • [17] M. Chihaoui, A. Elkefi, W. Bellil, and C. Ben Amar, “A Survey of 2D Face Recognition Techniques,” Computers, vol. 5, no. 4, p. 21, 2016, doi: 10.3390/computers5040021.
  • [18] J. Jose, P. Poornima, and K. Kumar, “A Novel Method for Color Face Recognition Using KNN Classifier,” International Conference on Computing, Communication and Applications, 2012, [Online]. Available: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6179151
  • [19] H. Yıldız, O. Torunoglu, and S. Sayardemir, “Hiyerarşik Gruplandırma ile Kızılötesi Bölgede Yüz Tanıma Performansının Arttırılması Increasing the Performance of Face Recognition in Near Infrared Region with Hierarchical Grouping,” pp. 1–4, 2015.
  • [20] A. K. Junoh, M. N. Mansor, S. A. Abu, W. Z. Wan Ahmad, A. Z. Mukhtar, and S. F. Fauzi, “Home security system based on k-NN classifier,” Procedia Eng, vol. 38, pp. 1811–1815, 2012, doi: 10.1016/j.proeng.2012.06.223.
  • [21] I. Pattnaik, A. Dev, and A. K. Mohapatra, “A face recognition taxonomy and review framework towards dimensionality, modality and feature quality,” Eng Appl Artif Intell, vol. 126, no. PC, p. 107056, 2023, doi: 10.1016/j.engappai.2023.107056.
  • [22] L. Li, X. Mu, S. Li, and H. Peng, “A Review of Face Recognition Technology,” IEEE Access, vol. 8, pp. 139110–139120, 2020, doi: 10.1109/ACCESS.2020.3011028.
  • [23] F. Boutros, V. Struc, J. Fierrez, and N. Damer, “Synthetic data for face recognition: Current state and future prospects,” Image Vis Comput, vol. 135, p. 104688, 2023, doi: 10.1016/j.imavis.2023.104688.
  • [24] H. Qiu, B. Yu, D. Gong, Z. Li, W. Liu, and D. Tao, “SynFace: Face Recognition with Synthetic Data,” Proceedings of the IEEE International Conference on Computer Vision, pp. 10860–10870, 2021, doi: 10.1109/ICCV48922.2021.01070.
  • [25] Y. Qi and B. Min, “Study on Face Recognition Scheme for Improving the Way of Material Handover,” Proceedings -2021 International Conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2021, pp. 385–388, 2021, doi: 10.1109/ICEITSA54226.2021.00080.
  • [26] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Dec. 2015.
  • [27] R. Socher et al., “ImageNet: A large-scale hierarchical image database,” 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255, 2009, doi: 10.1109/CVPR.2009.5206848.
  • [28] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Sep. 2014.
  • [29] H.-W. Ng and S. Winkler, “A data-driven approach to cleaning large face datasets,” in 2014 IEEE International Conference on Image Processing (ICIP), IEEE, Oct. 2014, pp. 343–347. doi: 10.1109/ICIP.2014.7025068.
  • [30] Omkar M. Parkhi, Andrea Vedaldi, and Andrew Zisserman, “VGG Face Dataset,” in British Machine Vision Conference, SWANSEA, 2015.
  • [31] D. King, “Dlib Library.” Accessed: Oct. 29, 2023. [Online]. Available: http://dlib.net/
  • [32] Davis. E. King, “Dlib-ml: A Machine Learning Toolkit,” Journal of Machine Learning Research, vol. 10, pp. 1755–1758, 2009, doi: 10.1145/1577069.1755843.
  • [33] L. R. Cerna, “Face Detection: Histogram of Oriented Gradients and Bag of Feature Method,” International Conference on Image Processing, pp. 657–661, 2013.
  • [34] R. Cerd Ng, K. Ming Lim, C. Poo Lee, and S. F. Abdul Razak, “Surveillance system with motion and face detection using histograms of oriented gradients,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 14, no. 2, p. 869, May 2019, doi: 10.11591/ijeecs.v14.i2.pp869-876.
  • [35] O. Déniz, G. Bueno, J. Salido, and F. De la Torre, “Face recognition using Histograms of Oriented Gradients,” Pattern Recognit Lett, vol. 32, no. 12, pp. 1598–1603, Sep. 2011, doi: 10.1016/j.patrec.2011.01.004.
  • [36] H. Zhang and G. Chen, “The Research of Face Recognition Based on PCA and K-Nearest Neighbor,” in 2012 Symposium on Photonics and Optoelectronics, IEEE, May 2012, pp. 1–4. doi: 10.1109/SOPO.2012.6270975.
  • [37] I. Dokmanic, R. Parhizkar, J. Ranieri, and M. Vetterli, “Euclidean Distance Matrices: Essential Theory, Algorithms and Applications,” Feb. 2015, doi: 10.1109/MSP.2015.2398954.
  • [38] C. G. Turhan and H. S. Bilge, “Class-wise two-dimensional PCA method for face recognition,” vol. 11, pp. 286–300, 2017, doi: 10.1049/iet-cvi.2016.0135.
  • [39] H. S. Dadi and G. K. Mohan Pillutla, “Improved Face Recognition Rate Using HOG Features and SVM Classifier,” IOSR Journal of Electronics and Communication Engineering, vol. 11, no. 04, pp. 34–44, 2016, doi: 10.9790/2834-1104013444.
  • [40] Q. Cao, Y. Ying, and P. Li, “Similarity metric learning for face recognition,” Proceedings of the IEEE International Conference on Computer Vision, pp. 2408–2415, 2013, doi: 10.1109/ICCV.2013.299.
  • [41] S. Valuvanathorn, S. Nitsuwat, and M. L. Huang, “Multi-feature face recognition based on PSO-SVM,” International Conference on ICT and Knowledge Engineering, pp. 140–145, 2012, doi: 10.1109/ICTKE.2012.6408543.

FACE RECOGNITION APPROACH BY USING DLIB AND K-NN

Year 2023, Volume: 1 Issue: 2, 93 - 103, 02.02.2024

Muhammed Taha Aydın Oğuzhan Menemencioğlu İlhami Muharrem Orak

Abstract

The face serves as a unique topographical map that reflects an individual's distinct features. Face recognition has gained prominence as a popular biometric method, especially in security control applications. In this study, we present a system developed using a Haar cascade classifier and Hog-based Dlib face detector for human face detection. Face features are extracted with the Dlib deep metric learning library, and classification is performed using the k-NN algorithm. The system underwent testing on benchmark data within the framework of an exam access control system. The system demonstrated an accuracy of up to 90% in the Orl_Face dataset. The measurement results were compared with other face recognition systems for validation. Beyond accuracy assessments, the proposed system was also compared with similar training tools, fostering a comprehensive discussion on its performance and capabilities.

Keywords

Face recognition Dlib Hog k-NN Exam admission control

References

  • [1] B. Girod and D. I. Processing, “Image Processing and Related Fields,” no. 22, [Online]. Available: http://fourier.eng.hmc.edu/e161/lectures/e161ch1.pdf
  • [2] T. Prabaharan, P. Periasamy, V. Mugendiran, and Ramanan, “Studies on application of image processing in various fields: An overview,” IOP Conf Ser Mater Sci Eng, vol. 961, p. 012006, Nov. 2020, doi: 10.1088/1757-899X/961/1/012006.
  • [3] W. Dhifli and A. B. Diallo, “Face Recognition in the Wild,” Procedia Comput Sci, vol. 96, pp. 1571–1580, 2016, doi: 10.1016/j.procs.2016.08.204.
  • [4] Y. Barlas, “Havalimanına da ‘yüz tanıma’ geliyor.,” Gazete Habertürk, 2016.
  • [5] N. Singla, G. Singla, and N. Maggon, “Face Detection and Recognition Using Digital Image Processing: ‘State of the Art,’” in 2022 2nd International Conference on Innovative Sustainable Computational Technologies (CISCT), IEEE, Dec. 2022, pp. 1–4. doi: 10.1109/CISCT55310.2022.10046441.
  • [6] Ösym, “Ösym Yüz Tanıma,” Ösym. [Online]. Available: http://www.osym.gov.tr/TR,12816/osymden-elektroniksinavlarda-yuz-tanima-donemi-30112016.html
  • [7] N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” Proceedings - 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. I, pp. 886–893, 2005, doi: 10.1109/CVPR.2005.177.
  • [8] F. Karakaya, H. Altun, and A. Çavuşlu, “Gerçek Zamanlı Nesne Tanıma Uygulamaları için HOG Algoritmasının FPGA Tabanlı Gömülü Sistem Uyarlaması Implementation of HOG algorithm for Real Time Object Recognition Applications on FPGA based Embedded System”.
  • [9] P. Viola and M. 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, vol. 1, pp. I-511-I–518, 2001, doi: 10.1109/CVPR.2001.990517.
  • [10] M. A. Turk and A. P. Pentland, “Face recognition using eigenfaces,” Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 586–591, 1991, doi: 10.1109/CVPR.1991.139758.
  • [11] H. Sultan, M. H. Zafar, S. Anwer, A. Waris, H. Ijaz, and M. Sarwar, “Real Time Face Recognition Based Attendance System For University Classroom,” 2nd IEEE International Conference on Artificial Intelligence, ICAI 2022, no. March 2021, pp. 165–168, 2022, doi: 10.1109/ICAI55435.2022.9773650.
  • [12] K.Kanimozhi, “Biometric Authentication for Online Examination,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 3, no. 9, 2015, [Online]. Available: https://www.ijircce.com/upload/2015/september/149_Biometric.pdf
  • [13] A. Fayyoumi and A. Zarrad, “Novel Solution Based on Face Recognition to Address Identity Theft and Cheating in Online Examination Systems,” Scientific Research, 2014.
  • [14] A. Raghuwanshi and P. D. Swami, “An automated classroom attendance system using video based face recognition,” in 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), IEEE, May 2017, pp. 719–724. doi: 10.1109/RTEICT.2017.8256691.
  • [15] A. R. S. Siswanto, A. S. Nugroho, and M. Galinium, “Implementation of face recognition algorithm for biometrics based time attendance system,” Proceedings - 2014 International Conference on ICT for Smart Society: “Smart System Platform Development for City and Society, GoeSmart 2014”, ICISS 2014, pp. 149–154, 2014, doi: 10.1109/ICTSS.2014.7013165.
  • [16] J. Hu, J. Lu, and Y.-P. Tan, “Discriminative Deep Metric Learning for Face Verification in the Wild,” in 2014 IEEE Conference on Computer Vision and Pattern Recognition, IEEE, Jun. 2014, pp. 1875–1882. doi: 10.1109/CVPR.2014.242.
  • [17] M. Chihaoui, A. Elkefi, W. Bellil, and C. Ben Amar, “A Survey of 2D Face Recognition Techniques,” Computers, vol. 5, no. 4, p. 21, 2016, doi: 10.3390/computers5040021.
  • [18] J. Jose, P. Poornima, and K. Kumar, “A Novel Method for Color Face Recognition Using KNN Classifier,” International Conference on Computing, Communication and Applications, 2012, [Online]. Available: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6179151
  • [19] H. Yıldız, O. Torunoglu, and S. Sayardemir, “Hiyerarşik Gruplandırma ile Kızılötesi Bölgede Yüz Tanıma Performansının Arttırılması Increasing the Performance of Face Recognition in Near Infrared Region with Hierarchical Grouping,” pp. 1–4, 2015.
  • [20] A. K. Junoh, M. N. Mansor, S. A. Abu, W. Z. Wan Ahmad, A. Z. Mukhtar, and S. F. Fauzi, “Home security system based on k-NN classifier,” Procedia Eng, vol. 38, pp. 1811–1815, 2012, doi: 10.1016/j.proeng.2012.06.223.
  • [21] I. Pattnaik, A. Dev, and A. K. Mohapatra, “A face recognition taxonomy and review framework towards dimensionality, modality and feature quality,” Eng Appl Artif Intell, vol. 126, no. PC, p. 107056, 2023, doi: 10.1016/j.engappai.2023.107056.
  • [22] L. Li, X. Mu, S. Li, and H. Peng, “A Review of Face Recognition Technology,” IEEE Access, vol. 8, pp. 139110–139120, 2020, doi: 10.1109/ACCESS.2020.3011028.
  • [23] F. Boutros, V. Struc, J. Fierrez, and N. Damer, “Synthetic data for face recognition: Current state and future prospects,” Image Vis Comput, vol. 135, p. 104688, 2023, doi: 10.1016/j.imavis.2023.104688.
  • [24] H. Qiu, B. Yu, D. Gong, Z. Li, W. Liu, and D. Tao, “SynFace: Face Recognition with Synthetic Data,” Proceedings of the IEEE International Conference on Computer Vision, pp. 10860–10870, 2021, doi: 10.1109/ICCV48922.2021.01070.
  • [25] Y. Qi and B. Min, “Study on Face Recognition Scheme for Improving the Way of Material Handover,” Proceedings -2021 International Conference on Electronic Information Technology and Smart Agriculture, ICEITSA 2021, pp. 385–388, 2021, doi: 10.1109/ICEITSA54226.2021.00080.
  • [26] K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Dec. 2015.
  • [27] R. Socher et al., “ImageNet: A large-scale hierarchical image database,” 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248–255, 2009, doi: 10.1109/CVPR.2009.5206848.
  • [28] K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition,” Sep. 2014.
  • [29] H.-W. Ng and S. Winkler, “A data-driven approach to cleaning large face datasets,” in 2014 IEEE International Conference on Image Processing (ICIP), IEEE, Oct. 2014, pp. 343–347. doi: 10.1109/ICIP.2014.7025068.
  • [30] Omkar M. Parkhi, Andrea Vedaldi, and Andrew Zisserman, “VGG Face Dataset,” in British Machine Vision Conference, SWANSEA, 2015.
  • [31] D. King, “Dlib Library.” Accessed: Oct. 29, 2023. [Online]. Available: http://dlib.net/
  • [32] Davis. E. King, “Dlib-ml: A Machine Learning Toolkit,” Journal of Machine Learning Research, vol. 10, pp. 1755–1758, 2009, doi: 10.1145/1577069.1755843.
  • [33] L. R. Cerna, “Face Detection: Histogram of Oriented Gradients and Bag of Feature Method,” International Conference on Image Processing, pp. 657–661, 2013.
  • [34] R. Cerd Ng, K. Ming Lim, C. Poo Lee, and S. F. Abdul Razak, “Surveillance system with motion and face detection using histograms of oriented gradients,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 14, no. 2, p. 869, May 2019, doi: 10.11591/ijeecs.v14.i2.pp869-876.
  • [35] O. Déniz, G. Bueno, J. Salido, and F. De la Torre, “Face recognition using Histograms of Oriented Gradients,” Pattern Recognit Lett, vol. 32, no. 12, pp. 1598–1603, Sep. 2011, doi: 10.1016/j.patrec.2011.01.004.
  • [36] H. Zhang and G. Chen, “The Research of Face Recognition Based on PCA and K-Nearest Neighbor,” in 2012 Symposium on Photonics and Optoelectronics, IEEE, May 2012, pp. 1–4. doi: 10.1109/SOPO.2012.6270975.
  • [37] I. Dokmanic, R. Parhizkar, J. Ranieri, and M. Vetterli, “Euclidean Distance Matrices: Essential Theory, Algorithms and Applications,” Feb. 2015, doi: 10.1109/MSP.2015.2398954.
  • [38] C. G. Turhan and H. S. Bilge, “Class-wise two-dimensional PCA method for face recognition,” vol. 11, pp. 286–300, 2017, doi: 10.1049/iet-cvi.2016.0135.
  • [39] H. S. Dadi and G. K. Mohan Pillutla, “Improved Face Recognition Rate Using HOG Features and SVM Classifier,” IOSR Journal of Electronics and Communication Engineering, vol. 11, no. 04, pp. 34–44, 2016, doi: 10.9790/2834-1104013444.
  • [40] Q. Cao, Y. Ying, and P. Li, “Similarity metric learning for face recognition,” Proceedings of the IEEE International Conference on Computer Vision, pp. 2408–2415, 2013, doi: 10.1109/ICCV.2013.299.
  • [41] S. Valuvanathorn, S. Nitsuwat, and M. L. Huang, “Multi-feature face recognition based on PSO-SVM,” International Conference on ICT and Knowledge Engineering, pp. 140–145, 2012, doi: 10.1109/ICTKE.2012.6408543.
There are 41 citations in total.

Details

Primary Language English
Subjects Image Processing, Deep Learning
Journal Section Research Article
Authors

Muhammed Taha Aydın This is me

Oğuzhan Menemencioğlu 0000-0002-4343-6563

İlhami Muharrem Orak 0000-0002-7219-4209

Publication Date February 2, 2024
Submission Date October 20, 2023
Acceptance Date December 4, 2023
Published in Issue Year 2023 Volume: 1 Issue: 2

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