CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Face Recognition using PCA and SVM with Surf Technique

by Shilpa Sharma, Kumud Sachdeva
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 129 - Number 4
Year of Publication: 2015
Authors: Shilpa Sharma, Kumud Sachdeva
10.5120/ijca2015906832

Shilpa Sharma, Kumud Sachdeva . Face Recognition using PCA and SVM with Surf Technique. International Journal of Computer Applications. 129, 4 ( November 2015), 41-46. DOI=10.5120/ijca2015906832

@article{ 10.5120/ijca2015906832,
author = { Shilpa Sharma, Kumud Sachdeva },
title = { Face Recognition using PCA and SVM with Surf Technique },
journal = { International Journal of Computer Applications },
issue_date = { November 2015 },
volume = { 129 },
number = { 4 },
month = { November },
year = { 2015 },
issn = { 0975-8887 },
pages = { 41-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume129/number4/23065-2015906832/ },
doi = { 10.5120/ijca2015906832 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:22:34.253436+05:30
%A Shilpa Sharma
%A Kumud Sachdeva
%T Face Recognition using PCA and SVM with Surf Technique
%J International Journal of Computer Applications
%@ 0975-8887
%V 129
%N 4
%P 41-46
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face Recognition is a biometric application which can be controlled through hybrid systems instead of a solitary procedure. This paper focus at Principal Component Analysis system alongside SVM and SURF for Face Recognition. Preprocessing abrogates improper, superflous and unnecessary information. PCA naturally decreases dimensionality and Feature extraction to minimize highlights. Furthermore, after element extraction, the recognition is performed on these elements to perceive the person. SVM classifier is a classifier which is utilized as a part of this paper for performing the recognition capacity and SURF is utilized for matching the source image with the database. This outcomes in an adequate error rate and accuracy furthermore this gives better MSE and PSNR results. In this paper, a novel facial methodology is used to hunt the element space down the ideal component subset where elements are extricated by PCA , while matching and recognition is done utilizing SVM classifier and SURF Technique. For the usage of this proposed work we utilize Image Processing Toolbox under the MATLAB programming.

References
  1. Gheorghita Ghinea, Rajkumar kannan, and Suresh kannaiyan,”Gradient-Orientation-Based PCA Subspace for Novel Face Recognition”in IEEE Access, vol 2,2169-3536, 2014.
  2. Hossein Sahoolizadeh, Zargham Heidari, and Hamid Dehghani,” A New Face Recognition Method using PCA, LDA and Neural Network “ International Journal of Electrical and Electronics Engineering 2:8, 2008.
  3. Erik Murphy-Chutorian and Mohan Manubhai Trivedi,” Head Pose Estimation in Computer Vision: A Survey”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008.
  4. Ajit P. Gosavi , S.R.Khot,” Facial Expression Recognition using Principal Component Analysis with Singular Value Decomposition”in International Journal of Advance Research in Computer Science and Management Studies, Volume 1, Issue 6, November 2013.
  5. Dong Hui, Han Dian Yuan,” Research of Image Matching Algorithm Based on SURF Features ”,in International Conference on Computer Science and Information Processing (CSIP) ,2012.
  6. S.V.N. Vishwanathan, M. Narasimha Murty,” SSVM : A Simple SVM Algorithm”.
  7. R.Gowthamam, C. Sathish, “Recognition of Occluded Facial Images Using Texture Features at SURF Keypoints”, International Journal of Emerging Technology and Advanced Engineering Website (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Special Issue 2, April 2014).
  8. Tudor Barbu, “Gabor Filter-based Face Recognition Technique”, Proceedings of the Romanian Academy, Series A, Volume 11, Number 3/2010, pp. 277–283.
  9. Amina Khatun and Md. Al-Amin Bhuiyan, “Neural Network based Face Recognition with Gabor Filters”, IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.1, January 2011.
  10. Mounika B.R, Reddy N.J and Reddy V.B.D , “A Neural Network based Face Detection using Gabor Filter Response”, IJNN (International Journal of Neural Networks ) ISSN: 2249-2763 & ESSN: 2249-2771, Volume 2, Issue 1, 2012, pp.-06-09.
  11. Hung-Fu Huang and Shen-Chuan Tai, “Facial Expression Recognition Using New Feature Extraction Algorithm”, Electronic Letters on Computer Vision and Image Analysis 11(1):41-54; 2012.
  12. Thai Hoang Le, Len Bui, “ Face Recognition Based on SVM and 2DPCA”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 4, No. 3, September, 2011.
  13. D Murugan, Dr. S Arumugam, K Rajalakshmi and Manish T I, “ Performance Evaluation of Face Recognition using Gabor Filter , Log Gabor Filter and Discrete Wavelet Transform”, IJCSIT(International Journal of Computer Science and Information Technology) Vol .2, No. 1,Feb 2010.
  14. Avinash Kaushal , J P S Raina, “Face Detection using Neural Network & Gabor Wavelet Transform”, IJCST Vol. 1, Issue 1, September 2010.
  15. Saqib Hayat, Abdul Basit Siddiqui and Sajid Ali Khan , “ A Study of Feature Subset Selection Methods for Dimension Reduction”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.7, No.1 (2014), pp.251-266.
  16. Mr. Hamid M. Hasan, Prof. Dr. Waleed A. AL.Jouhar & Dr. Majed A. Alwan, “Face Recognition Using Improved FFT Based Radon by PSO and PCA Techniques”, International Journal of Image Processing (IJIP), Volume (6) : Issue (1) : 2012 .
  17. Rabab M. Ramadan and Rehab F. Abdel – Kader, “Face Recognition Using Particle Swarm Optimization-Based Selected Features”, International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 2, No. 2, June 2009.
  18. Nitish Sinha, Priyanka.G, “Implementation of Binary PSO Based Face Recognition System using Image Preprocessing”, International Conference on Signal, Image Processing and Applications IPCSIT vol.21 (2011).
  19. Aneesh M U, Abhishek A K Masand, K Manikantan , “Optimal Feature Selection based on Image Pre-processing using Accelerated Binary Particle Swarm Optimization for Enhanced Face Recognition”, International Conference on Communication Technology and System Design 2011.
  20. P.V Shinde, B.L. Gunjal, “ Particle Swarm Optimization- Best Feature Selection method for Face Images”, International Journal of Scientific & Engineering Research Volume 3, Issue 8, August-2012 ISSN 2229-5518.
  21. Dr.S.B.Thorat, S.K.Nayak and Miss.Jyoti P Dandale, “Facial Recognition Technology: An analysis with scope in India”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 1, 2010.
  22. Dattatr V.Jadhao, Raghunath S.Holambe; "Feature Extraction and Dimensionality Reduction Using Radon and Fourier Transform with Application to Face Recognition", International Conference on Computational Intelligence and Multimedia Application 2007.
  23. Zhao, W., Chellappa, R., Phillips, P. J., and Rosenfeld, A., \Face recognition: A literature survey," ACM Comput. Survey. 35(4), 399{458 (2003).
  24. Veeramachaneni, K., Peram, T., Mohan, C., and Osadciw, L. A., \Optimization using particle swarm with near neighbourinteractions," in [GECCO 2003, Lecture Notes Computer Science, Springer Verlag], 2723/2003 (2003).
  25. Perez, C., \New algorithms generate improved templates for biometric recognition," (Apr 2007).
  26. Veeramachaneni, K., Osadciw, L., and Kamath, G., \Probabilistically driven particle swarms for discrete multi valued problems: Design and analysis," in [Proceedings of IEEE Swarm Intelligence Symposium], (April 1-5, 2007).
  27. Phillips, P. J., Moon, H., Rizvi, S. A., and Rauss, P. J., \The feret evaluation methodology for face-recognition algorithms," IEEE Trans. Pattern sAnal. Mach. Intel. 22(10), 1090{1104 (2000).
  28. Turk, M. and Pentland, A., Eigenfaces for recognition," Journal of Cognitive Neuroscience Vol.3(1),No. 71{86} ,1991.
  29. Martinez, A. M. and Kak, A. C., \PCA versus LDA," IEEE Transactions on Pattern Analysis and Machine Intelligence 23(2), 228{233 (2001).
  30. Lu, J., Plataniotis, K. N., and Venetsanopoulos, A. N., \Face recognition using LDA-based algorithms," IEEE Trans. on Neural Networks Vol.14, No.195 ,January 2003.
  31. Alec Banks, et al. "A review of particle swarm optimization. Part II: hybridization, combinatorial, multi criteria and constrained optimization, and indicative applications", Nat Compute (2008) 7:109-124, DOI 10.1007/s11047-007-9050-z.
Index Terms

Computer Science
Information Sciences

Keywords

Face Recognition Principal component Analysis Support Vector Machine SURF.

Powered by PhDFocusTM