Abstract

Nowadays, face recognition has become very much popular to recognize a person with their face in order to avoid crime, etc. This mechanism is based on the division of the face, processing into three phases, i.e. face detection, feature extraction based on the input face image, and face recognition. In this paper, the authors have compared three algorithms, such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Patterns (LBP). The rate of accuracy of face recognition has also been compared. The advantages and disadvantages of these algorithms will help in obtaining a solution, so that a better face recognition system can be designed.

Keywords
DCT, LBP, PCA, LDA
How to Cite this Article?
Dewangan, R., and Verma, S. (2016). A Review On The Comparison Of Global Features Based Techniques Lda, Pca, And Lbp Algorithm For Face Recognition. i-manager’s Journal on Pattern Recognition, 3(3), 32-38.
References
[1]. Mattew Turk and Alex Pentland, (1991). “Eigenfaces for Recognition”. Journal of Cognitive Neuroscience, Vol.3, No.1, pp.71-86.
[2]. Kirby and Sirovich, (1990). “Application of Karhunen- Loeve procedure for the characterization of human faces”. IEEE Trans. Pattern Analysis and Machine Intelligence, Vol.12, No.1, pp.103-108.
[3]. Turk, M.A., and A.L. Pentland, (1991). “Face recognition using Eigen faces”. Proc. IEEE Computer Society Conference Computer Vision and Pattern Recognition, pp.586-591.
[4]. Kyungim Baek, Bruce A. Draper, J. Ross Beveridge, and Kai She, (2002). “PCA vs. ICA: A Comparison on the FERET Data Set”. Proceedings of the 6th Joint Conference on Information Science (JCIS), pp.824-827.
[5]. T. Chen, W. Yin, X.-S. Zhou, D. Comaniciu, and T.S. Huang, (2006). “Total Variation Models for variable Lighting Face Recognition and uneven Background Correction”. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.28, No.9, pp.1519-1524.
[6]. Longin Jan Latecki, Venugopal Rajagopal, and Ari Gross, (2005). “Image Retrieval and Reversible Illumination Normalization”. Electronic Imaging 2005. International Society for Optics and Photonics, pp.99- 110.
[7]. P.J.B. Hancock, V. Bruce, and A.M. Burton, (1997). “Testing Principal Component Representations for Faces”. Proc. of 4th Neural Computation and Psychology Workshop, Springer, London, pp.84-97.
[8]. Jonathon Shlens, (2005). “A tutorial on Principal Component Analysis”. arXiv preprint arXiv:1404.1100. Systems Neurobiology Laboratory, Vol.2.
[9]. Zhujie, and Y.L. Yu, (1994). “Face recognition with Eigen faces”. Proc. IEEE Intl. Conf. Industrial Technol, Guangzhou, pp.434-438.
[10]. Delipersad, S.C. and A.D Broadhurst, (1997). “Face recognition using neural networks”. Proc. IEEE Communication Signal Processing (COMSIG'97), pp.33- 36.
[11]. Nazish, et al., (2001). “Face recognition using neural networks”. Proc. IEEE INMIC 2001, pp.277-281.
[12]. D.E Rumelhart, G.E. Hinton, and R.J. Williams, (1985). “Learning internal representation by error propagation”. In D.E. Rumelhart and J.L. Mcclelland, Eds, Parallel Distributed Processing Exploration in Microstructure in Cognition, pp.318-362.
[13]. Kilian Q. Weinberger, and Lawrence K. Saul, (2009). “Distance metric learning for large margin nearest neighbor classification”. Journal of Machine Learning Research, Vol.10, pp.207-244.
[14]. T. Kanade, (1973). Picture Processing by Computer Complex and Recognition of Human Faces. Doctoral Dissertation, University of Kyoto, Japan.
[15]. D. Zhang, S. Li, and D. Gatica-Perez, (2004). “Realtime face detection using boosting learning in hierarchical feature spaces”. In Proceedings of the International Conference on Pattern Recognition (ICPR), Cambridge, UK, pp.411-414.
[16]. P. Viola and M. Jones (2001). “Rapid object detection using a boosted cascade of simple features”. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Kauai, HI, USA. pp.511-518.
[17]. T. Ojala, M. Pietikäinen, and T. Mäenpää, (2002). “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns”. IEEE Transactions on Pattern Analysis and Machine intelligence, Vol.24, No.7, pp.971-987.
[18]. Y. Freund and R.E. Schapire, (1996). “Experiments with a new boosting algorithm”. In Proceedings of the IEEE International Conference on Machine Learning (ICML), Vol.96, pp.148-156.
[19]. B. Fröba and A. Ernst, (2004). “Face detection with the modified census transform”. In Proceedings of the 6th IEEE International Conference on Automatic Face and Gesture Recognition (AFGR), pp.91-96.
[20]. H. Jin, Q. Liu, H. Lu, and X. Tong, (2004). “Face detection using improved LBP under Bayesian framework”. In Proc. Third International Conference on Image and Graphics (ICIG), pp.306-309.
[21]. T. Ahonen, A. Hadid, and M. Pietikäinen, (2004). “Face recognition with local binary patterns”. In Proc. 8th European Conference on Computer Vision (ECCV), Prague, Czech Republic, pp.469-481
[22]. G. Zhang, X. Huang, S.Z. Li, Y. Wang, and X. Wu, (2004). “Boosting Local Binary Pattern (LBP) based face recognition”. In Proc. Advances in Biometric Person Authentication: 5th Chinese Conference on Biometric Recognition, Guangzhou, China, pp.179-186.
[23]. M. Turtinen, M. Pietikäinen, and O. Silven, (2005). “Visual characterization of paper using isomap and local binary patterns”. IEICE Transactions on Information and Systems, Vol.89, No.7, pp.2076-2083.
[24]. V. Takala, T. Ahonen, and M. Pietikäinen, (2005). “Block-based methods for image retrieval using local binary patterns”. In Proc. 14th Scandinavian Conference on Image Analysis (SCIA), Joensuu, Finland, pp.882-891.
[25]. M. Heikkilä, M. Pietikäinen, and J. Heikkilä, (2004). “A texture-based method for detecting moving objects”. In Proc. 15th British Machine Vision Conference (BMVC), Vol.401, pp.187-196.
[26]. T. Ojala, M. Pietikäinen, and D. Harwood, (1996). “A comparative study of texture measures with classification based on feature distributions”. Pattern Recognition, Vol.29, No.1, pp.51-59.
[27]. R. Zabih and J. Woodfill, (1994). “Non-parametric local transforms for computing visual correspondence”. In Proceedings of the Third European Conference on Computer Vision, Springer Berlin Heidelberg, pp.151-158.
[28]. X. Huang, S.Z. Li, and Y. Wang, (2004). “Shape localization based on statistical method using extended local binary pattern”. In Proc. Third International Conference on Image and Graphics (ICIG), pp.184-187.
[29]. Turk, M.A., and Pentland, A.P. (1991). “Face recognition using eigenfaces”. In Computer Vision and Pattern Recognition, 1991. Proceedings CVPR'91., IEEE Computer Society Conference, pp.586-591.LNCS 3021, pp.469-481.
Username / Email
Password
Don't have an account?  Sign Up
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.

Purchase Instant Access

PDF
10
USD

250
INR

HTML
10
USD

250
INR


We strive to bring you the best. Your feedback is of great value to us. Feel free to post your comments and suggestions.