Call for Paper - November 2019 Edition
IJCA solicits original research papers for the November 2019 Edition. Last date of manuscript submission is October 21, 2019. Read More

Survey Paper on the Timeline of Face Detection Techniques

Print
PDF
IJCA Proceedings on National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering
© 2015 by IJCA Journal
ACEWRM 2015 - Number 1
Year of Publication: 2015
Authors:
Smita Marwadi
Avishkar Anand
Himadri Singh
Barkha Rani Pandey

Smita Marwadi, Avishkar Anand, Himadri Singh and Barkha Rani Pandey. Article: Survey Paper on the Timeline of Face Detection Techniques. IJCA Proceedings on National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering ACEWRM 2015(1):16-21, May 2015. Full text available. BibTeX

@article{key:article,
	author = {Smita Marwadi and Avishkar Anand and Himadri Singh and Barkha Rani Pandey},
	title = {Article: Survey Paper on the Timeline of Face Detection Techniques},
	journal = {IJCA Proceedings on National Conference Potential Research Avenues and Future Opportunities in Electrical and Instrumentation Engineering},
	year = {2015},
	volume = {ACEWRM 2015},
	number = {1},
	pages = {16-21},
	month = {May},
	note = {Full text available}
}

Abstract

Research scholars have found face recognition as an important area for their work because our face is a combination of various features (facial features )like the eyes ,ears ,nose etc. Various research works have been also carried out in this area. This paper focuses on a brief survey over the currently employed techniques of face recognition along with their advantages and disadvantages applications and constraints. Some of the most general methods include Eigenface (Eigenvectors or PCA), Neural Networks, Geometric Based Template Matching approaches etc. Further this paper performs an analysis on these face recognition approaches in order to constitute face representations . This survey also deals with the factors affecting the recognition processes, recognition rates and other features that affect the face detection and face recognition techniques.

References

  • Fakhreddine Karray, Milad Alemzadeh, Jamil Abou Saleh and Mo Nours Arab "Human-Computer Interaction: Overview on State of the Art" International Journal On Smart Sensing and Intelligent Systems, Vol. 1, No. 1, March 2008.
  • A Survey: Face Recognition Techniques Muhammad Sharif, Sajjad Mohsin and Muhammad Younas Javed ,Research Journal of Applied Sciences, Engineering and Technology 4(23):4979- 4990, 2012.
  • M. A. Turk and A. Pentland "Eigen faces for recognition" Journal of cognitive Neuroscience, March 1991.
  • Lawrence, S. , C. L. Giles, A. C. Tsoi and A. D. Back, 1997. Face recognition: A convolutional neural-network approach. IEEE T. Neural Networks. , 8: 98-113.
  • Zhao, H. and P. Yuen, 2008. Incremental linear discriminant analysis for face recognition. IEEE T. Syst. Man Cy. B. , 38(1): 210-221
  • Hastie, T. , R. Tibshirani and A. Buja, 1994. Flexible discriminant analysis by optimal scoring. J. Amer. Statist. Assoc. , 89: 1255-1270.
  • Baudat, G. and F. Anouar, 2000. Generalized discriminant analysis using a kernel approach. Neural Comput. , 12: 2385-2404.
  • Chen, W. , P. Yuen, J. Huang and D. Dai, 2005. Kernel machine-based one-parameter regularized fisher discriminant method for face recognition. IEEE T. Syst. Man Cy. B. , 35(4): 659-669.
  • www. en. wikipedia. org
  • A Literature Survey on Face Recognition Techniques, International Journal of Computer Trends and Technology (IJCTT) – volume 5 number 4 –Nov 2013,
  • Ming-Hsuan Yang, David J. Kriegman and NarendraAhuja, "Detecting Faces in Images: A Survey," IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 24, NO. 1, JANUARY 2002.
  • Lin-Lin Huang, Akinobu Shimizu, Yoshihiro Hagihara, Hidefumi Kobatake ,"Face detection from cluttered images using a polynomial neural network", Elsevier Science 2002
  • U. KreQel, J. SchRurmann, "Pattern classification techniques based on function approximation, in: H. Bunke, P. S. P. Wang (Eds. ), Handbook of Character Recognition and Document Image Analysis", World Scienti5c, Singapore, 1997, pp. 49–78.
  • Yue Ming, Qiuqi Ruan, Xiaoli Li, Meiru Mu, " Efficient Kernel Discriminate Spectral Regression for 3D Face Recognition", Proceedings Of ICSP 2010
  • R. A. Fisher, "The Use of Multiple Measurements in Taxonomic Problems", 1936.
  • A Literature Survey on Face Recognition Techniques, International Journal of Computer Trends and Technology (IJCTT) – volume 5 number 4 –Nov 2013.
  • Jyoti S. Bedre ,Shubhangi Sapkal, "Comparative Study of Face Recognition Techniques: A Review", Emerging Trends in Computer Science and Information Technology – 2012(ETCSIT2012) Proceedings published in International Journal of Computer Applications® (IJCA) 12
  • Sushma Jaiswal, Dr. (Smt. ) Sarita Singh Bhadauria, Dr. Rakesh Singh Jadon," COMPARISON BETWEEN FACE RECOGNITION ALGORITHM-EIGENFACES, FISHERFACES AND ELASTIC BUNCH GRAPH MATCHING", Volume 2, No. 7, July 2011 Journal of Global Research in Computer Science.
  • R. Bruneli and T. Poggio, "Face recognition: features versus templates," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, pp. 1042-1052, 1993.
  • www. scholarpedia. org/article/Elastic_Bunch_Graph_Matching
  • I. J. Cox, J. Ghosn, and P. N. Yianios, "Feature-Based face recognition using mixture-distance," Computer Vision and Pattern Recognition,1996.
  • B. S. Manjunath, R. Chellappa, and C. von der Malsburg, "A Feature based approach to face recognition," Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 373-378, 1992.
  • Vetter, T. and T. Poggio, 1997. Linear object classes and image synthesis from a single example image. IEEE T. Pattern Anal. , 19(7): 733-742.
  • Bustard, J. D. and M. S. Nixon. 2010. 3D morphable model construction for robust ear and face recognition. IEEE Conference on Computer Vision and Pattern Recognition, CVPR 10, San Francisco, pp: 2582-2589.
  • Shu-Fan, W. and L. Shang-Hong, 2011. Reconstructing 3D face model with associated expression deformation from a single faceimage via constructing a low-dimensional deformation manifold. IEEE T. Pattern Anal. 33(10): 2115-2121.
  • Unsang, P. , T. Yiying and A. K. A. K. Jain, 2010. Age-invariant face recognition. IEEE T. Pattern Anal. , 32(5): 947-54.