A Study on Video Based Face Recognition for Real-Time Surveillance

Authors

  • Prof. A. V. Deorankar  Department of Computer Science and Engineering, Government College of Engineering, Amravati, India
  • Neha S. Tadam  Department of Computer Science and Engineering, Government College of Engineering, Amravati, India

Keywords:

HOG Features, Face Recognition, Feed forward, Back propagation Neural Network, Principal Component Analysis; Surveillance Video.

Abstract

Now a days it is require to increase real time security because some threats may affect seriously individual life. For enhancing security numerous security techniques have been introduced. We review all of these techniques. Some techniques are unable to address some issues. A video surveillance system overcome most of challenges addressed in existing system with minimum complexity. This study aims primarily to propose a facial recognition system to identify a person using his face. Video Dataset is from an environment in real time. The facial features selected and a common multi-layer feed the neural network for classification. The extracted characteristics are determined and shown to the neural network as a pattern vector. Facial image data collected are compared to facial images in the database. If the data does not match, an alarm or signal is generated that alerts security personnel to action. A security alarm or signal.

References

  1. Z. Shao, J. Cai, and Z. Wang, "Smart Monitoring Cameras Driven Intelligent Processing to Big Surveillance Video Data," IEEE Transactions on Big Data, vol. 4, pp. 105-116, 2018.
  2. M. A. Abuzneid and A. Mahmood, "Enhanced Human Face Recognition Using LBPH Descriptor, Multi-KNN, and Back-Propagation Neural Network," IEEE Access, vol. 6, pp. 20641-20651, 2018.
  3. B. S. Satari, N. A. A. Rahman, and Z. M. Z. Abidin, "Face recognition for security efficiency in managing and monitoring visitors of an organization," in 2014 International Symposium on Biometrics and Security Technologies (ISBAST), 2014, pp. 95-101.
  4. N. Jamil, S. Lqbal, and N. Iqbal, "Face recognition using neural networks," in Proceedings. IEEE International Multi Topic Conference, 2001. IEEE INMIC 2001. Technology for the 21st Century., 2001, pp. 277-281.
  5. K. Bong, S. Choi, C. Kim, D. Han, and H. Yoo, "A Low-Power Convolutional Neural Network Face Recognition Processor and a CIS Integrated With Always-on Face Detector," IEEE Journal of Solid-State Circuits, vol. 53, pp. 115-123, 2018.
  6. Y. Kim, H. Kim, S. Kim, H. Kim, and S. Ko, "Illumination normalisation using convolutional neural network with application to face recognition," Electronics Letters, vol. 53, pp. 399-401, 2017.
  7. D. Malik and S. Bansal, "Face Recognition Based on Principal Component Analysis and Linear Discriminant Analysis " International Journal of Research in Electronics and Communication Technology (IJRECT 2016), vol. 3, pp. 7-10, June 2016.
  8. D. Meena and R. Sharan, "An approach to face detection and recognition," in 2016 International Conference on Recent Advances and Innovations in Engineering (ICRAIE), 2016, pp. 1-6.
  9. Bhattacharyya, Suman & Rahul, Kumar. (2013). Face recognition by linear discriminant analysis. International Journal of Communication Network Security. 2. 31-35.
  10. Javed, Ali. (2013). Face Recognition Based on Principal Component Analysis. International Journal of Image, Graphics and Signal Processing. 5. 38. 10.5815/ijigsp.2013.02.06.
  11. F. Z. Chelali, A. Djeradi, and R. Djeradi, "Linear discriminant analysis for face recognition," in 2009 International Conference on Multimedia Computing and Systems, 2009, pp. 1-10.
  12. A. J. Dhanaseely, S. Himavathi, and E. Srinivasan, "Performance comparison of cascade and feed forward neural network for face recognition system," in International Conference on Software Engineering and Mobile Application Modelling and Development (ICSEMA 2012), 2012, pp. 1-6.
  13. K. Vikram and S. Padmavathi, "Facial parts detection using Viola Jones algorithm," in 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), 2017, pp. 1-4.
  14. Divya Malik, Shaloo Bansal, “Face Recognition Based on Principal Component Analysis and Linear Discriminant Analysis”, International Journal of Research in Electronics and Communication Technology (IJRECT 2016)
  15. R. Arroyo, J. J. Yebes, L. M. Bergasa, I. G. Daza, and J. Almazán, "Expert video-surveillance system for real-time detection of suspicious behaviors in shopping malls," Expert Systems with Applications, vol. 42, pp. 7991-8005, 2015/11/30/ 2015.
  16. S. C. Loke, "Astronomical Image Acquisition Using an Improved Track and Accumulate Method," IEEE Access, vol. 5, pp. 9691-9698, 2017.
  17. M. P. Rajath Kumar, R. Keerthi Sravan, and K. M. Aishwarya, "Artificial neural networks for face recognition using PCA and BPNN," in TENCON 2015 - 2015 IEEE Region 10 Conference, 2015, pp. 1-6.
  18. M. Slavkovi? and D. Jevti?, "Face Recognition Using Eigenface Approach," SERBIAN JOURNAL OF ELECTRICAL ENGINEERING, vol. 9, pp. 1211-130, 2012.
  19. H. M. Desai and V. Gandhi, "A survey on Background subtraction techniques," International Journal of Scientific & Engineering Research, vol. 5, pp. 1365–1367, 2014.
  20. Y. Wong, S. Chen, S. Mau, C. Sanderson, and B. C. Lovell, "Patch-based Probabilistic Image Quality Assessment for Face Selection and Improved Video-based Face Recognition," in IEEE Biometrics Workshop, Computer Vision and Pattern Recognition (CVPR) Workshops, ed, June 2011, pp. 81-88.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2019-06-30

Issue

Volume 7, Issue 3, May-June-2020

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Prof. A. V. Deorankar, Neha S. Tadam "A Study on Video Based Face Recognition for Real-Time Surveillance" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 3, pp.547-552, May-June-2020.