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A Face Authentication System Using Landmark Detection

Year 2021, Volume: 1 Issue: 1, 28 - 34, 30.08.2021

Abstract

Biometric data is the key for many security applications. Authentication relies on the individual’s measurable biometric properties collected as features. In this study, a face authentication system is built to be used in opening the entrance door accessing to the apartments and housing estates. The proposed system consists of three stages. In the first stage, landmarks on the face are captured using a deep neural network. Then six selected features from the landmarks are extracted and traditional machine learning algorithms are used to authenticate users. In the last stage, a user interface is built. Face recognition tests achieved an accuracy rate of 89.79%.

References

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  • [26] R. Feng and B. Prabhakaran, “A novel method for post-surgery face recognition using sum of facial parts recognition,” IEEE Winter Conference on Applications of Computer Vision, 2014, pp. 1082-1089.
  • [27] T. Dietterich, “Overfitting and undercomputing in machine learning,” ACM Computing Surveys, vol. 27, no. 3, pp. 326-327, 1995.
  • [28] N. Cristianini and J. S. Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, 2000.
  • [29] Python bindings for the Qt cross platform application toolkit, [online] https://pypi.org/project/PyQt5/, [Accessed: June 27, 2021]
Year 2021, Volume: 1 Issue: 1, 28 - 34, 30.08.2021

Abstract

References

  • [1] A. K. Jain, A. Ross, and S. Pankanti, “Biometrics: A tool for information security,” IEEE Transactions on Information Forensics and Security, vol. 1, no. 2, pp. 125-143, June 2006.
  • [2] R. Spolaor, Q. Li, M. Monaro, M. Conti, L. Gamberini, and G. Sartori, “Biometric authentication methods on smartphones: A survey,” PsychNology Journal, vol. 14, no. 2-3, pp. 87-98, 2016.
  • [3] S. B. Thorat, S. K. Nayak, and J. P. Dandale, “Facial recognition technology: An analysis with scope in India,” arXiv preprint, arXiv:1005.4263, 2010.
  • [4] C. A. Hansen, Face Recognition, Institute for Computer Science University of Tromso, Norway, 2009.
  • [5] A. K. Jain and S. Z. Li, Handbook of Face Recognition, vol. 1, New York: Springer, 2011.
  • [6] Wikipedia, The Free Encyclopedia, [online] Available at: https://en.wikipedia.org/wiki/Facial_recognition_system, [Accessed: June 21, 2021].
  • [7] L. Li, X. Mu, S. Li, and H. Peng, “A review of face recognition technology,” IEEE Access, vol. 8, pp. 139110-139120, 2020.
  • [8] C. Li, R. Wang, J. Li, and L. Fei, “Face detection based on YOLOv3,” Recent Trends in Intelligent Computing, Communication and Devices, vol. 1006, pp. 277-284, 2020.
  • [9] C. Gürel, Development of a Face Recognition System, Master of Science Thesis, Atilim University, 2011.
  • [10] M. Turk, M and A. Pentland, “Eigenfaces for recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
  • [11] R. E. Schapire, Explaining Adaboost, In: Empirical Inference, Springer, Berlin, Heidelberg, pp. 37-52, 2013.
  • [12] H. A. Rowley, S. Baluja, and T. Kanade, “Neural network-based face detection,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 23-38, 1998.
  • [13] W. S. Noble, “What is a support vector machine?,” Nature Biotechnology, vol. 24, no. 12, pp. 1565-1567, 2006.
  • [14] C. Liu and H. Wechsler, “Independent component analysis of Gabor features for face recognition,” IEEE Transactions on Neural Networks, vol. 14, pp. 919-928, 2003.
  • [15] A. H. Boualleg, C. Bencheriet, and H. Tebbikh, “Automatic face recognition using neural network-PCA,” In Proc. 2nd Information and Communication Technologies (ICTTA), Damascus, Syria, 2006, pp. 1920-1925.
  • [16] Y. Song, Y. Kim, U. Chang, and H. B. Kwon, “Face recognition robust to left-right shadows facial symmetry,” Pattern Recognition, vol. 39, pp. 1542-1545, 2006.
  • [17] P. S. Prasad, R. Pathak, V. K. Gunjan, and H. V. R. Rao, “Deep learning based representation for face recognition,” ICCCE 2019, Lecture Notes in Electrical Engineering, vol. 570, Springer, Singapore, 2020, pp. 419-424.
  • [18] O. M. Parkhi, A. Vedaldi, and A. Zisserman, “Deep face recognition,” In X. Xie, M. W. Jones, and G. K. L. Tam, editors, Proceedings of the British Machine Vision Conference (BMVC), 2015, pp. 41.1-41.12.
  • [19] X. Wu, R. He, Z. Sun, and T. Tan, “A light CNN for deep face representation with noisy labels,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 11, pp. 2884-2896, 2018.
  • [20] W. J. Baddar, J. Son, D. H. Kim, S. T. Kim, and Y. M. Ro, “A deep facial landmarks detection with facial contour and facial components constraint,” In IEEE International Conference on Image Processing (ICIP), 2016, pp. 3209-3213.
  • [21] M. Köstinger, P. Wohlhart, P. M. Roth, and H. Bischof, “Annotated facial landmarks in the wild: A large-scale, real-world database for facial landmark localization,” In IEEE International Conference on Computer Vision Workshops (ICCV Workshops), 2011, pp. 2144-2151.
  • [22] N. Agarwal, A. Krohn-Grimberghe, and R. Vyas, “Facial key points detection using deep convolutional neural networkNaimishNet,” arXiv preprint, arXiv:1710.00977, 2017.
  • [23] Y. Bengio, “Facial keypoints detection,” [Online] Available at: https://www.kaggle.com/c/facial-keypoints-detection, [Accessed: August 21, 2021].
  • [24] E. P. Prokopakis, I. M. Vlastos, V. A. Picavet, T. G. Nolst, R. Thomas, C. Cingi, and P. W. Hellings, “The golden ratio in facial symmetry,” Rhinology, vol. 51, no. 1, pp. 18-21, 2013.
  • [25] P. S. Gaikwad, V. B. Kulkarni, “Face recognition using golden ratio for door access control system,” In: Merchant S. N., Warhade K., Adhikari D. (eds), Advances in Signal and Data Processing, Lecture Notes in Electrical Engineering, vol. 703. Springer, Singapore, 2021, pp. 209-231.
  • [26] R. Feng and B. Prabhakaran, “A novel method for post-surgery face recognition using sum of facial parts recognition,” IEEE Winter Conference on Applications of Computer Vision, 2014, pp. 1082-1089.
  • [27] T. Dietterich, “Overfitting and undercomputing in machine learning,” ACM Computing Surveys, vol. 27, no. 3, pp. 326-327, 1995.
  • [28] N. Cristianini and J. S. Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, 2000.
  • [29] Python bindings for the Qt cross platform application toolkit, [online] https://pypi.org/project/PyQt5/, [Accessed: June 27, 2021]
There are 29 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence
Journal Section Research Articles
Authors

Velican Ercan This is me

M. Erdal Özbek

Publication Date August 30, 2021
Submission Date July 16, 2021
Published in Issue Year 2021 Volume: 1 Issue: 1

Cite

IEEE V. Ercan and M. E. Özbek, “A Face Authentication System Using Landmark Detection”, Journal of Artificial Intelligence and Data Science, vol. 1, no. 1, pp. 28–34, 2021.

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