Review of Face Recognition Systems Using Different Artificial Neural Network Algorithms

Authors

  • Sameer Ahmad Mir  Department of Electronics and Communication Engineering , BGSB University Rajouri J & K , India
  • Sahil Nazir Pottoo  Department of Electronics and Communication Engineering , BGSB University Rajouri J & K , India
  • Tahir Mohammad Wani  Department of Electronics and Communication Engineering , BGSB University Rajouri J & K , India
  • Muneer Ahmad Dar  Department of Electronics and Communication Engineering , BGSB University Rajouri J & K , India

Keywords:

Biometric system, ANN, Image processing, Pattern recognition.

Abstract

Face recognition is one of the most relevant applications of image processing and biometric systems. Artificial neural networks (ANN) have been used in the field of image processing and pattern recognition. In the past few years, face recognition has received a significant attention and regarded as one of the most successful applications in the field of image analysis. The human faces represent complex, multidimensional, meaningful visual stimulant. Developing a computational model for face recognition is difficult. Face detection can be regarded as fundamental part of face recognition systems according to its ability to focus computational resources on the part of an image containing a face. The process of face detection in images is complex because of variability present across human faces such as: pose; expression; position and orientation; skin color ; presence of glasses or facial hair; differences in camera gain; lighting conditions; and image resolution . The analysis of facial expression was primarily a research field for psychologists in the past years. At the same time, advances in many domains such as: face detection; tracking; and recognition; pattern recognition; and image processing contributed significantly to research in automatic facial expression recognition. Therefore, this research includes a general review of face recognition studies and systems, which based on different ANN approaches and algorithms

References

  1. W. Zhao, et al (2000) “Face recognition: a literature survey”, Technical Report CAR-TR-948, University of Maryland, October 2000.
  2.  Phil Brimblecombe (2002) “Face Detection using Neural Networks”, H615 – Meng Electronic Engineering, School of Electronics and Physical Sciences, URN: 1046063.
  3. P. Viola & M.J. Jones (2001) “Robust real-time object detection”, Technical Report CRL/2001/01, Cambridge Research Laboratory, USA, February 2001
  4. Ming-Hsuan Yan, et al (2002), “Detecting Faces in Images: A Survey”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 1, pp34-58, January2002
  5. Minyoung Kim, et.al (2008) “Face tracking and recognition with visual constraints in real-world videos”, IEEE Conference on Computer Vision and Pattern Recognition, pp23-28, June.
  6. Yao-Jiunn Chen & Yen-Chun Lin (2007) “Simple Face-detection Algorithm Based on Minimum Facial Features”, The 33rd Annual Conference of the IEEE Industrial Electronics Society (IECON) 58 Nov 2007, Taipei, Taiwan, pp455-460.
  7. W. Zhao et al (2003) “Face recognition: A literature survey”, ACM Computing Surveys, Vol.35, No.4, December 2003, pp. 399–458.
  8.  Cha Zhang & Zhengyou Zhang (2010) “A Survey of Recent Advances in Face Detection”, Technical Report, MSR-TR-2010-66, Microsoft Research, Microsoft Corporation One Microsoft Way, Redmond,WA98052, ttp://www.research.microsoft
  9. ZIAD M. HAFED AND MARTIN D. LEVINE,” Face Recognition Using the Discrete Cosine Transform”, International Journal of Computer Vision 43(3), 167–188, 2001.
  10.  MengJooEr,Weilong Chen, and Shiqian Wu,” High -Speed Face Recognition Based on Discrete Cosine Transform and RBF Neural Networks” IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 16, NO. 3, MAY 2005.
  11.  RabiaJafri and Hamid R. Arabnia,”ASurvey of Face Recognition Techniques”, Journal of Information Processing Systems, Vol.5, No.2, June 2009.
  12. FatmaZohraChelali, Amar Djeradi,”Face Recognition System using Discrete Cosine Transform Combined with MLP and RBF Neural Networks”, International Journal of Mobile Computing and Multimedia Communications, Vol.4, No.4, December 2012
  13. Tej Pal Singh, “Face Recognition by using Feed Forward Back Propagation Neural Network”, International Journal of Innovative Research in Technology & Science, vol.1, no.1,
  14.  N.Revathy, T.Guhan,“Facerecognition system using back propagation artificial neural networks”, International Journal of Advanced Engineering Technology, vol.3, no.1, 2012.
  15. H.Schneiderman andT. Kanade,“A statistical method for 3D object detection applied to faces and cars,”IEEE conference on Computer vision and pattern recognition,13-15 June2000,Hilton Head Island,pp. 746–751.
  16. Ling-Zhi Liao, Si-Wei Luo, and Mei Tian “Whitened faces Recognition With PCA andICA”IEEE Signal Processing Letters, vol. 14, n. 12, pp1008-1011, Dec. 2007.
  17. Kwan-Ho Lin, Kin-Man Lam, and Wan-Chi Siu. “A New Approach using ModiGiedHausdorff Distances with Eigen Face for Human Face Recognition” IEEE Seventh international Conference on Control, Automation, Robotics and Vision,Singapore,2-5Dec 2002,pp 980-984.
  18. Zdravko Liposcak,Sven Loncaric,“Face Recognition From Profiles Using Morphological Operations”, IEEE Conference on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems, 1999.
  19. S.-H. Lin, S. Y. Kung, and L.-J. Lin, “A probabilistic DBNN with applications to sensor fusion and object recognition,” in Proc. 5th IEEE Wkshp. Neural Networks Signal Processing , Cambridge, MA, Aug 1995, pp. 333–342.
  20. S. Y. Kung and J. S. Taur, “Decision-based neural networks with signal/image classification applications,” IEEE Trans. Neural Networks, vol. 6, pp. 170–181, Jan 1995.
  21. S. Y. Kung, M. Fang, S. P. Liou, and J. S. Taur, “Decision-based neural network for face recognition system,” in Proc. 1995 IEEE Int. Conf. Image Processing, Washington, D.C., vol. I, Oct 1995, pp. 430–433.
  22.  S.-H. Lin and S. Y. Kung, “Probabilistic DBNN via expectation-maximization with multisensor classification applications,” in Proc.1995 IEEE Int. Conf. Image Processing , Washington, D.C., vol. III, Oct 1995, pp. 236–239.
  23.  S.-H. Lin, Y. Chan, and S. Y. Kung, “A probabilistic decision-based neural network for locating deformable objects and its applications to surveillance system and video browsing,” in tProc. Int. Conf. Acoust., Speech. Signal Processing, Atlanta, GA, May 1996.
  24. B. Moghaddam and A. Pentland, “Probabilistic visual learning for object detection,” in Proc. 5th Int. Conf. Comput. Vision, Cambridge, MA, June 1995.
  25. Hudson, Hagan and Demuth, Neural Network Toolbox™ User’s Guide R2012a, The MathWorks, Inc., 3 Apple Hill Drive Natick, MA 01760-2098, 2012, www.mathworks.com.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2018-04-25

Issue

Volume 4, Issue 1, March-April-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

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

How to Cite

[1]
Sameer Ahmad Mir, Sahil Nazir Pottoo, Tahir Mohammad Wani, Muneer Ahmad Dar, " Review of Face Recognition Systems Using Different Artificial Neural Network Algorithms, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 1, pp.166-171, March-April-2018.