Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

Study of different Trends and Techniques in Face Recognition

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 96 - Number 8
Year of Publication: 2014
Divyakant T. Meva
C. K. Kumbharana

Divyakant T Meva and C K Kumbharana. Article: Study of different Trends and Techniques in Face Recognition. International Journal of Computer Applications 96(8):1-4, June 2014. Full text available. BibTeX

	author = {Divyakant T. Meva and C. K. Kumbharana},
	title = {Article: Study of different Trends and Techniques in Face Recognition},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {96},
	number = {8},
	pages = {1-4},
	month = {June},
	note = {Full text available}


History of Face recognition is old enough to be mature. In 1960s, face recognition became semi-automated. In 1970s, face recognition took another step in automation. In 1988, first semi-automated facial recognition system was deployed. In 2001, automated face recognition captured attention of public at SuperBowl event to capture surveillance images. Now a day, every country in the world is using this technology for different purposes. In this paper, we have discussed some novel techniques and algorithms for face recognition of the current trends.


  • Zhao W. et al. , Face Recognition – A Literature Survey, ACM Computing Surveys, Vol. 35, No. 4, December 2003, pp. 399–458
  • Turk, M. and Pentland, A. , Eigenfaces for recognition, J. Cogn. Neurosci. 3, 1991, 72–86
  • Moghaddam, B. and Pentlad, A. , Probabilistic visual learning for object representation, IEEE Trans. Patt. Anal. Mach. Intell. 19,1997, 696– 710.
  • Belhumeur P N, Hespanha J P and Kreigman D J, Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Patt. Anal. Mach. Intell. 19, 2007, 711–720.
  • Liu C, Andwechsler H, Evolutionary pursuit and its application to face recognition, IEEE Trans. Patt. Anal. Mach. Intell. 22, 2000a. , 570–582
  • Lin, S. H. , Kung, S. Y. , And Lin, L. J. , Face recognition/ detection by probabilistic decision based neural network, IEEE Trans. Neural Netw. 8, 1997, 114–132
  • Kelly, M. D. , Visual identification of people by computer, Tech. rep. AI-130, Stanford AI Project, Stanford, CA.
  • Samaria, F. And Young, S. , HMM based architecture for face identification, Image Vis. Comput. 12, 1994, 537–583.
  • Lawrence, S. , Giles, C. L. , Tsoi, A. C. , And Back, A. D. , Face recognition: A Convolutional neural-network approach. IEEE Trans. Neural Netw. 8, 1997, 98–113
  • Pentland, A. , Moghaddam, B. , Starner, T. , View-based And modular eigenspaces for face recognition. In Proceedings, IEEE Conference on Computer Vision and Pattern Recognition, 1994.
  • Heisele, B. , Serre, T. , Pontil, M. , And Poggio, T. , Component-based face detection. In Proceedings, IEEE Conference on Computer Vision and Pattern Recognition, 2001.
  • Liau et al. , " New parallel Methods of Face Recognition", Advances in Face Recognition, I-Tech, 2008, pp 15-26
  • Taranpreet Singh Ruprah, "Face Recognition Based on PCA Algorithm", Special Issue of International Journal of Computer Science & Informatics, ISSN: 2231–5292, Vol. - II, Issue-1, 2 , pp 221 - 225
  • Muhammad Akmal Khan et al. , "Face Recognition using Sub – Holistic PCA", British Journal of Science, September 2011, Vol. 1 (1), pp. 111- 120
  • Craw, I. , Tock, D. & Bennett, A. Finding Face Features, in 'European Conference on Computer Vision', 1992, pp. 92–96.
  • Manjunath, B. , Chellappa, R. & von der Malsburg, C. (1992), 'A Feature Based Approach to Face Recognition', IEEE Conference Proceedings on Computer Vision and Pattern Recognition pp. 373–378.
  • Lades, M. , Vorbr¨uggen, J. , Buhmann, J. , Lange, J. , von der Malsburg, C. ,W¨urtz, R. & Konen,W. , 'Distortion invariant object recognition in the dynamic link architecture', IEEE Transactions on Computers 42(3),1993, 300–311.
  • Wiskott, L. , Fellous, J. , Kr¨uger, N. & von der Malsburg, C. , Face recognition by elastic bunch graph matching, in L. C. Jain et al. , ed. , 'Intelligent Biometric Techniques in Fingerprint and Face Recognition',1999,CRC Press, chapter 11, pp. 355–396.
  • Sarala Ramkumar & Silambarasan Kaliamoorthi, "A Hybrid Approach to Face Recognition under Varying Illumination", IJCSET, April 2011, Vol. 1, Issue 3,113-117
  • Rui Huang et al, " A Hybrid Face Recognition Method using Markov Random Fields".