CFP last date
16 December 2024
Reseach Article

Face Recognition: A Literature Review

by Nawaf Hazim Barnouti, Sinan Sameer Mahmood Al-dabbagh, Wael Esam Matti
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 11 - Number 4
Year of Publication: 2016
Authors: Nawaf Hazim Barnouti, Sinan Sameer Mahmood Al-dabbagh, Wael Esam Matti
10.5120/ijais2016451597

Nawaf Hazim Barnouti, Sinan Sameer Mahmood Al-dabbagh, Wael Esam Matti . Face Recognition: A Literature Review. International Journal of Applied Information Systems. 11, 4 ( Sep 2016), 21-31. DOI=10.5120/ijais2016451597

@article{ 10.5120/ijais2016451597,
author = { Nawaf Hazim Barnouti, Sinan Sameer Mahmood Al-dabbagh, Wael Esam Matti },
title = { Face Recognition: A Literature Review },
journal = { International Journal of Applied Information Systems },
issue_date = { Sep 2016 },
volume = { 11 },
number = { 4 },
month = { Sep },
year = { 2016 },
issn = { 2249-0868 },
pages = { 21-31 },
numpages = {9},
url = { https://www.ijais.org/archives/volume11/number4/935-2016451597/ },
doi = { 10.5120/ijais2016451597 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:04:04.754525+05:30
%A Nawaf Hazim Barnouti
%A Sinan Sameer Mahmood Al-dabbagh
%A Wael Esam Matti
%T Face Recognition: A Literature Review
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 11
%N 4
%P 21-31
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face recognition have gained a great deal of popularity because of the wide range of applications such as in entertainment, smart cards, information security, law enforcement, and surveillance. It is a relevant subject in pattern recognition, computer vision, and image processing. Two major methods are used for features extraction, which can be classified into appearance-based and Model-based methods. Appearance-based methods use global representations to identify a face. Model-based face methods aim to construct a model of the human face that capture facial variations. Image similarity is the distance between the vectors of two images. This paper contains Four sections. The first section discusses face recognition applications with examples. The second section discuss the common feature face recognition methods. The third section discuss distance measurement classifiers. The fourth section discuss different face recognition databases.

References
  1. Jain, A. K. and Li, S. Z. , 2011. Handbook of face recognition. New York: springer.
  2. Dewi Agushinta, R. and Septadepi, I. , Face Recognition System Using Eigenface Method based on Facial Component Region.
  3. Pornpanomchai, C. and Inkuna, C. , 2010, February. Human face recognition by euclidean distance and neural network. In Second International Conference on Digital Image Processing. International Society for Optics and Photonics. pp. 754603-754603.
  4. Zhao, W. , Chellappa, R. , Phillips, P. J. and Rosenfeld, A. , 2003. Face recognition: A literature survey. ACM computing surveys (CSUR), 35(4), pp. 399-458.
  5. Chellappa, R. , Wilson, C. L. and Sirohey, S. , 1995. Human and machine recognition of faces: A survey. Proceedings of the IEEE, 83(5), pp. 705-741.
  6. Jafri, R. and Arabnia, H. R. , 2009. A survey of face recognition techniques. Jips, 5(2), pp. 41-68.
  7. Lu, X. , 2003. Image analysis for face recognition, personal notes. Dept. of Computer Science and Engineering.
  8. Shah, D. H. , Shah, D. J. and Shah, D. T. V. , 2014. The Exploration of Face Recognition Techniques. International Journal of Application or Innovation in Engg. And Management (IJAIEM) Web Site: www. ijaiem. org Email: editor@ ijaiem. org, editorijaiem@ gmail. com, 3(2).
  9. Parmar, D. N. and Mehta, B. B. , 2014. Face Recognition Methods & Applications. arXiv preprint arXiv:1403. 0485.
  10. Kurmi, U. S. , Agrawal, D. and Baghel, R. K. , Study of different face recognition algorithms and challenges. IJER, Volume, (3), pp. 112-115.
  11. Kadam, K. D. , 2014. Face Recognition using Principal Component Analysis with DCT. International Journal of Engineering Research and General Science, ISSN, pp. 2091-2730.
  12. Slavkovi?, M. and Jevti?, D. , 2012. Face recognition using eigenface approach. Serbian Journal of Electrical Engineering, 9(1), pp. 121-130.
  13. Sharma, N. and Dubey, 2014. A. P. S. K. , Face Recognition Analysis Using PCA, ICA And Neural Network. International Journal of Digital Application & Contemporary research, 2(9).
  14. Murtaza, M. , Sharif, M. , Raza, M. and Shah, J. , 2014. Face recognition using adaptive margin fisher's criterion and linear discriminant analysis. International Arab Journal of Information Technology, 11(2), pp. 1-11.
  15. Sahu, R. K. , Singh, Y. P. and Kulshrestha, A. , 2013. A Comparative Study of Face Recognition System Using PCA and LDA. International Journal of IT, Engineering and Applied Sciences Research, 2(10).
  16. Christry, C. , Pavithra, J. and Sathya, F. M. , 2014. Comparison on PCA ICA and LDA in Face Recognition. International Journal of Computing Algorithm,3, pp. 917-922.
  17. Naik, G. R. and Kumar, D. K. , 2011. An overview of independent component analysis and its applications. Informatica, 35(1).
  18. Wang, L. , Zhang, Y. and Feng, J. , 2005. On the Euclidean distance of images. IEEE transactions on pattern analysis and machine intelligence, 27(8), pp. 1334-1339.
  19. Taghizadegan, Y. , Ghassemian, H. and Naser-Moghaddasi, M. , 2012. 3D Face Recognition Method Using 2DPCA-Euclidean Distance Classification. ACEEE International Journal on Control System and Instrumentation, 3(1).
  20. Barnouti, N. H. , 2016. Improve Face Recognition Rate Using Different Image Pre-Processing Techniques. American Journal of Engineering Research (AJER), 5(4), pp. 46-53.
  21. Kapoor, S. , Khanna, S. and Bhatia, R. , 2010. Facial gesture recognition using correlation and mahalanobis distance. arXiv preprint arXiv:1003. 1819.
  22. Gawande, M. P. , and Agrawal, D. G. , 2014. Face recognition using PCA and different distance classifiers. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), 9(1), pp. 1-5.
  23. Abdullah, M. , Wazzan, M. and Bo-Saeed, S. , 2012. Optimizing face recognition using PCA. arXiv preprint arXiv:1206. 1515.
  24. Nicholl, P. and Amira, A. , 2008. DWT/PCA face recognition using automatic coefficient selection. In Electronic Design, Test and Applications, 2008. DELTA 2008. 4th IEEE International Symposium on, pp. 390-393. IEEE.
  25. Kshirsagar, V. P. , Baviskar, M. R. and Gaikwad, M. E. , 2011. March. Face recognition using Eigenfaces. In Computer Research and Development (ICCRD), 2011 3rd International Conference on, 2, pp. 302-306. IEEE.
  26. Ibrahim, R. and Zin, Z. M. , 2011. Study of automated face recognition system for office door access control application. In Communication Software and Networks (ICCSN), 2011 IEEE 3rd International Conference on, pp. 132-136. IEEE.
  27. Bhattacharyya, S. K. and Rahul, K. , 2013. Face Recognition By Linear Discriminant Analysis. International Journal of Communication Network Security, ISSN, pp. 2231-1882.
  28. Liang, Y. , Lai, J. H. , Zou, Y. X. , Zheng, W. S. and Yuen, P. C. , 2009, October. Face hallucination through KPCA. In Image and Signal Processing, 2009. CISP:09. 2nd International Congress on, pp. 1-5. IEEE.
  29. Wang, Y. and Zhang, Y. , 2010, November. Facial recognition based on kernel PCA. In Intelligent Networks and Intelligent Systems (ICINIS), 2010 3rd International Conference on, pp. 88-91. IEEE.
  30. Kamerikar, U. A. and Chavan, M. S. , 2014. Experimental Assessment of LDA and KLDA for Face Recognition. International Journal, 2(2).
  31. Zagouras, A. , Economou, G. , Macedonas, A. and Fotopoulos, S. , 2007, October. An application study of manifold learning-ranking techniques in face recognition. In Multimedia Signal Processing, 2007. MMSP 2007. IEEE 9th Workshop on, pp. 445-448. IEEE.
  32. Pal, M. and Foody, G. M. , 2010. Feature selection for classification of hyperspectral data by SVM. IEEE Transactions on Geoscience and Remote Sensing, 48(5), pp. 2297-2307.
  33. Sun, Y. , Chen, X. , Rosato, M. and Yin, L. , 2010. Tracking vertex flow and model adaptation for three-dimensional spatiotemporal face analysis. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 40(3), pp. 461-474.
  34. Jahanbin, S. , Choi, H. , Liu, Y. and Bovik, A. C. , 2008, September. Three dimensional face recognition using iso-geodesic and iso-depth curves. InBiometrics: Theory, Applications and Systems, 2008. BTAS 2008. 2nd IEEE International Conference on, pp. 1-6. IEEE.
  35. Malkauthekar, M. D. , Sapkal, S. D. and Kakarwal, S. N. , 2009, March. Experimental Analysis of Classification of Facial Images. In Advance Computing Conference, 2009. IACC 2009. IEEE International, pp. 1093-1098. IEEE.
  36. Sayeed, F. , Hanmandlu, M. and Ansari, A. Q. , 2011. Face recognition using segmental euclidean distance. Defence Science Journal, 61(5), pp. 431-442.
  37. Malkauthekar, M. D. , and Sapkal, S. D. , 2014. Comparision of mahalanobis and manhattan distance measures in pca based face recognition. International Journal of Computer & Technology (IJCET), 5(5), pp 01-11.
  38. Barnouti, N. H. , Al-Dabbagh, S. S. M. , Matti, W. E. , and Naser, M. A. S. , 2016. Face Detection and Recognition Using Viola-Jones with PCA-LDA and Square Euclidean Distance. International Journal of Advanced Computer Science and Applications(IJACSA), 7(5).
  39. Barnouti, N. H. , 2016. Face Recognition using PCA-BPNN with DCT Implemented on Face94 and Grimace Databases. 2016. International Journal of Computer Applications, 142(6), pp. 8-13.
  40. Nigam, A. and Gupta, P. , 2009. A new distance measure for face recognition system. In Image and Graphics, 2009. ICIG:09. Fifth International Conference on, pp. 696-701. IEEE.
  41. Kasar, M. M. , Bhattacharyya, D. and Kim, T. H. , 2016. Face Recognition Using Neural Network: A Review. International Journal of Security and Its Applications, 10(3), pp. 81-100.
  42. Bakhshi, Y. , Kaur, S. and Verma, P. , 2016. An Improvement in Face Recognition for Invariant Faces. 6(2), pp. 423-426.
  43. Poon, B. , Amin, M. A. and Yan, H. , 2016. Improved Methods on PCA Based Human Face Recognition for Distorted Images. In Proceedings of the International MultiConference of Engineers and Computer Scientists (1).
  44. Singh, N. A. , Kumar, M. B. and Bala, M. C. , 2016. Face Recognition System based on SURF and LDA Technique. International Journal of Intelligent Systems and Applications, 8(2).
  45. Bhat, V. S. and Pujari, J. D. , 2015. Human Face Recognition Using Combined Approaches PCA and ICA. Digital Image Processing, 7(10), pp. 301-307.
  46. Vinay, A. , Shekhar, V. S. , Murthy, K. B. and Natarajan, S. , 2015. Face Recognition Using Gabor Wavelet Features with PCA and KPCA-A Comparative Study. Procedia Computer Science, 57, pp. 650-659.
  47. Hu, G. , Chan, C. H. , Yan, F. , Christmas, W. and Kittler, J. , 2014, September. Robust face recognition by an albedo based 3D morphable model. InBiometrics (IJCB), 2014 IEEE International Joint Conference on, pp. 1-8. IEEE.
  48. Hjelmås, E. , 2000. Feature-based face recognition. In NOBIM Proceedings (Norwegian Image Processing and Pattern Recognition Conference).
  49. Sodhi, K. S. and Lal, M. , 2013. Comparative analysis of PCA-based face recognition system using different distance classifiers. Int. J. of Appl. or Innovation in Eng. & Manag, 2, pp. 341-348.
  50. Huang, G. B. , Ramesh, M. , Berg, T. and Learned-Miller, E. , 2007. Labeled faces in the wild: A database for studying face recognition in unconstrained environments, 1(2). Technical Report 07-49, University of Massachusetts, Amherst.
  51. Zou, J. , Ji, Q. and Nagy, G. , 2007. A comparative study of local matching approach for face recognition. IEEE Transactions on image processing, 16(10), pp. 2617-2628.
  52. Gottumukkal, R. and Asari, V. K. , 2004. An improved face recognition technique based on modular PCA approach. Pattern Recognition Letters, 25(4), pp. 429-436.
  53. Milborrow, S. , Morkel, J. and Nicolls, F. , 2010. The MUCT landmarked face database. Pattern Recognition Association of South Africa, 201(0).
  54. Kamerikar, U. A. and Chavan, M. S. , 2014. Experimental Assessment of LDA and KLDA for Face Recognition. International Journal, 2(2).
Index Terms

Computer Science
Information Sciences

Keywords

PCA LDA ICA KPCA KLDA EBGM