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Year 2021, Volume: 17 Issue: 2, 198 - 207, 31.12.2021

Abstract

References

  • J. Chris. "Biometrics—When the Person Is the Key." Sensor Review, 1992.
  • G. Matteo, D. Maio, and D. Malton. "On the error-reject trade-off in biometric verification systems." IEEE Transactions on Pattern Analysis and Machine Intelligence 19.7, 1997, pp. 786-796.
  • S. H. Lin, "An introduction to face recognition technology", Informing Sci. Int. J. an Emerg. Transdiscipl. 3, 2000, pp. 1-7.
  • W. Bledsoe, “Man-Machine Facial Recognition: Report on a Large-Scale Experiment”, Technical Report PRI 22, Panoramic Research, Inc., Palo Alto, California, 1966.
  • Ballantyne, Michael, Robert S. Boyer, and Larry Hines. "Woody bledsoe: His life and legacy." AI magazine 17.1, 1996, pp. 7-7.
  • M. Turk, A. Pentland, "Face recognition using eigenfaces." Proceedings, IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society, 1991.
  • L. Zhao, Y.H. Yang, “Theoretical analysis of illumination in pcabased vision systems,” Pattern Recognition, vol. 32, pp. 547-564, 1999.
  • A. Pentland, B. Moghaddam, T. Starner, “View-Based and modular eigenspaces for face recognition,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 1994, pp. 84-91.
  • D. G. Lowe, “Distinctive image features from scale-invariant keypoints”, International Journal of Computer Vision, 2004, pp: 91– 110.
  • R. G. Cinbis, J. Verbeek, C. Schmid. "Unsupervised metric learning for face identification in TV video", 2011 International Conference on Computer Vision. IEEE, 2011.
  • T. Ahonen, A. Hadid, M. Pietikainen, “Face description with local binary patterns: Application to face recognition”, TPAMI, 2006.
  • C. Lu, X. Tang, "Surpassing human-level face verification performance on LFW with GaussianFace." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 29. No. 1. 2015.
  • L. Wolf, T. Hassner, I. Maoz. "Face recognition in unconstrained videos with matched background similarity." CVPR 2011. IEEE, 2011.
  • Y. Gao, M. Leung. "Face recognition using line edge map." IEEE transactions on pattern analysis and machine intelligence 24.6, 2002, pp. 764-779.
  • N. Dalal, B. Triggs, “Histograms of oriented gradients for human detection”, In IEEE computer society conference on computer vision and pattern recognition, CVPR 2005 Vol. 1, 2005, pp. 886–893.
  • J. Sivic, M. Everingham, A. Zisserman. "“Who are you?”-Learning person specific classifiers from video." 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2009.
  • P.N. Belhumeur, J.P. Hespanha, D. J. Kriegman. "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection." IEEE Transactions on pattern analysis and machine intelligence 19.7, 1997, pp. 711-720.
  • M.S. Bartlett, "Independent component representations for face recognition." Face Image Analysis by Unsupervised Learning. Springer, Boston, MA, 2001 pp. 39-67.
  • I. Naseem, R. Togneri, M. Bennamoun. "Linear regression for face recognition." IEEE transactions on pattern analysis and machine intelligence 32.11, 2010, pp. 2106-2112.
  • L. Sirovich, M. Kirby. "Low-dimensional procedure for the characterization of human faces." Josa a 4.3, 1987, pp. 519-524.
  • M. Kirby, L. Sirovich. "Application of the Karhunen-Loeve procedure for the characterization of human faces." IEEE Transactions on Pattern analysis and Machine intelligence 12.1, 1990, pp. 103-108.
  • K. Fukushima and S. Miyake, "Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition" in Competition and Cooperation in Neural Nets., Springer, 1982, pp. 267-285.
  • O.M Parkhi, A. Vedaldi, A. Zisserman. "Deep face recognition.", 2015.
  • A. Krizhevsky, I. Sutskever and G. E. Hinton, "Imagenet classification with deep convolutional neural networks", NIPS, 2012.
  • Z. Qawaqneh, A.A. Mallouh, B. D. Barkana. "Deep convolutional neural network for age estimation based on VGG-face model." arXiv preprint arXiv: 1709.01664, 2017.
  • T. Zheng, W. Deng, J. Hu. "Cross-age lfw: A database for studying cross-age face recognition in unconstrained environments." arXiv preprint arXiv: 1708.08197, 2017.
  • F. Schroff, D. Kalenichenko, J. Philbin. "Facenet: A unified embedding for face recognition and clustering." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
  • T. Baltrušaitis, P. Robinson, and L. P. Morency. "Openface: an open source facial behavior analysis toolkit." 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2016.
  • B. Amos, B. Ludwiczuk, M. Satyanarayanan. "Openface: A general-purpose face recognition library with mobile applications." CMU School of Computer Science 6.2, 2016.
  • Y. Taigman, M. Yang, M. Ranzato, "Deepface: Closing the gap to human-level performance in face verification." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014, pp. 1701-1708.
  • D. Jiankang, J. Guo, N. Xue, S. Zafeiriou, "Arcface: Additive angular margin loss for deep face recognition." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.
  • I. Masi, Y. Wu, T. Hassner, P. Natarajan, "Deep face recognition: A survey." 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). IEEE, 2018.
  • W. Hao, Y. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, W. Liu, "Cosface: Large margin cosine loss for deep face recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, pp. 5265-5274.
  • Y. Wen, K. Zhang, Z. Li, Y.Qiao, "A comprehensive study on center loss for deep face recognition." International Journal of Computer Vision 127.6, 2019, pp: 668-683.
  • Y. Sun, X. Wang, X. Tang, “Deep learning face representation from predicting 10,000 classes.” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 1891– 1898.

An Application on Identification With The Face Recognition System

Year 2021, Volume: 17 Issue: 2, 198 - 207, 31.12.2021

Abstract

Measures taken in areas such as tracking personnel, patients, students and criminals, protecting mobile devices and combating fraud have evolved with technological developments in artificial intelligence. Today, face recognition systems are used as one of the fast and precise solutions determined for this need, since the identification of the person and identity in these problems requires instantaneous and high accuracy. These systems are generally created by comparing the features in the face images taken from the picture, historical or live video with the features in the real image of the person previously taken. Face recognition systems can be integrated into many applications, as person and identity verification may be required in almost every sector. In this study, a face recognition system was developed in order to verify the driver using public transportation in the transportation sector. In the current system, drivers start the journey by operating the vehicles with their own personnel cards. However, the driver who is authorized to use the vehicle may violate the rules by handing his personal card to an unauthorized driver and risk the driving. For this reason, it has been understood that the personnel cards are insufficient for driver authorization. In order to prevent any accident and violation caused by unauthorized driving, it has become necessary to add a personnel recognition and identity verification module to the system. For this requirement, after the driver has verified his biometric data, it was decided that the verification should be repeated instantaneously, throughout the ride and at certain intervals so that the driver does not give the ride to another driver. By avoiding the methods such as fingerprint reader and iris verification that will distract the driver and risk the driving, a facial recognition system has been created to provide control with video images taken while driving through cameras that are currently on the vehicles and see the driver. In order to check the accuracy of the relevant system, a separate database was created for each driver with the images taken from the videos during the driving at different times. Based on pre-trained deep learning networks with pictures representing drivers, the system was tested with test images in databases using tensorflow and opencv libraries. Thus, it has been observed that the face recognition module developed can increase driving safety with authorized and verified personnel on the smart transportation system.

References

  • J. Chris. "Biometrics—When the Person Is the Key." Sensor Review, 1992.
  • G. Matteo, D. Maio, and D. Malton. "On the error-reject trade-off in biometric verification systems." IEEE Transactions on Pattern Analysis and Machine Intelligence 19.7, 1997, pp. 786-796.
  • S. H. Lin, "An introduction to face recognition technology", Informing Sci. Int. J. an Emerg. Transdiscipl. 3, 2000, pp. 1-7.
  • W. Bledsoe, “Man-Machine Facial Recognition: Report on a Large-Scale Experiment”, Technical Report PRI 22, Panoramic Research, Inc., Palo Alto, California, 1966.
  • Ballantyne, Michael, Robert S. Boyer, and Larry Hines. "Woody bledsoe: His life and legacy." AI magazine 17.1, 1996, pp. 7-7.
  • M. Turk, A. Pentland, "Face recognition using eigenfaces." Proceedings, IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society, 1991.
  • L. Zhao, Y.H. Yang, “Theoretical analysis of illumination in pcabased vision systems,” Pattern Recognition, vol. 32, pp. 547-564, 1999.
  • A. Pentland, B. Moghaddam, T. Starner, “View-Based and modular eigenspaces for face recognition,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, 1994, pp. 84-91.
  • D. G. Lowe, “Distinctive image features from scale-invariant keypoints”, International Journal of Computer Vision, 2004, pp: 91– 110.
  • R. G. Cinbis, J. Verbeek, C. Schmid. "Unsupervised metric learning for face identification in TV video", 2011 International Conference on Computer Vision. IEEE, 2011.
  • T. Ahonen, A. Hadid, M. Pietikainen, “Face description with local binary patterns: Application to face recognition”, TPAMI, 2006.
  • C. Lu, X. Tang, "Surpassing human-level face verification performance on LFW with GaussianFace." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 29. No. 1. 2015.
  • L. Wolf, T. Hassner, I. Maoz. "Face recognition in unconstrained videos with matched background similarity." CVPR 2011. IEEE, 2011.
  • Y. Gao, M. Leung. "Face recognition using line edge map." IEEE transactions on pattern analysis and machine intelligence 24.6, 2002, pp. 764-779.
  • N. Dalal, B. Triggs, “Histograms of oriented gradients for human detection”, In IEEE computer society conference on computer vision and pattern recognition, CVPR 2005 Vol. 1, 2005, pp. 886–893.
  • J. Sivic, M. Everingham, A. Zisserman. "“Who are you?”-Learning person specific classifiers from video." 2009 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2009.
  • P.N. Belhumeur, J.P. Hespanha, D. J. Kriegman. "Eigenfaces vs. fisherfaces: Recognition using class specific linear projection." IEEE Transactions on pattern analysis and machine intelligence 19.7, 1997, pp. 711-720.
  • M.S. Bartlett, "Independent component representations for face recognition." Face Image Analysis by Unsupervised Learning. Springer, Boston, MA, 2001 pp. 39-67.
  • I. Naseem, R. Togneri, M. Bennamoun. "Linear regression for face recognition." IEEE transactions on pattern analysis and machine intelligence 32.11, 2010, pp. 2106-2112.
  • L. Sirovich, M. Kirby. "Low-dimensional procedure for the characterization of human faces." Josa a 4.3, 1987, pp. 519-524.
  • M. Kirby, L. Sirovich. "Application of the Karhunen-Loeve procedure for the characterization of human faces." IEEE Transactions on Pattern analysis and Machine intelligence 12.1, 1990, pp. 103-108.
  • K. Fukushima and S. Miyake, "Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition" in Competition and Cooperation in Neural Nets., Springer, 1982, pp. 267-285.
  • O.M Parkhi, A. Vedaldi, A. Zisserman. "Deep face recognition.", 2015.
  • A. Krizhevsky, I. Sutskever and G. E. Hinton, "Imagenet classification with deep convolutional neural networks", NIPS, 2012.
  • Z. Qawaqneh, A.A. Mallouh, B. D. Barkana. "Deep convolutional neural network for age estimation based on VGG-face model." arXiv preprint arXiv: 1709.01664, 2017.
  • T. Zheng, W. Deng, J. Hu. "Cross-age lfw: A database for studying cross-age face recognition in unconstrained environments." arXiv preprint arXiv: 1708.08197, 2017.
  • F. Schroff, D. Kalenichenko, J. Philbin. "Facenet: A unified embedding for face recognition and clustering." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
  • T. Baltrušaitis, P. Robinson, and L. P. Morency. "Openface: an open source facial behavior analysis toolkit." 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2016.
  • B. Amos, B. Ludwiczuk, M. Satyanarayanan. "Openface: A general-purpose face recognition library with mobile applications." CMU School of Computer Science 6.2, 2016.
  • Y. Taigman, M. Yang, M. Ranzato, "Deepface: Closing the gap to human-level performance in face verification." Proceedings of the IEEE conference on computer vision and pattern recognition. 2014, pp. 1701-1708.
  • D. Jiankang, J. Guo, N. Xue, S. Zafeiriou, "Arcface: Additive angular margin loss for deep face recognition." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019.
  • I. Masi, Y. Wu, T. Hassner, P. Natarajan, "Deep face recognition: A survey." 2018 31st SIBGRAPI conference on graphics, patterns and images (SIBGRAPI). IEEE, 2018.
  • W. Hao, Y. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, W. Liu, "Cosface: Large margin cosine loss for deep face recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, pp. 5265-5274.
  • Y. Wen, K. Zhang, Z. Li, Y.Qiao, "A comprehensive study on center loss for deep face recognition." International Journal of Computer Vision 127.6, 2019, pp: 668-683.
  • Y. Sun, X. Wang, X. Tang, “Deep learning face representation from predicting 10,000 classes.” In Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 1891– 1898.
There are 35 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Özlem Güven 0000-0003-0632-9301

Publication Date December 31, 2021
Submission Date June 18, 2021
Published in Issue Year 2021 Volume: 17 Issue: 2

Cite

APA Güven, Ö. (2021). An Application on Identification With The Face Recognition System. Electronic Letters on Science and Engineering, 17(2), 198-207.