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Reseach Article

Diagonally Assisted DCT Technique for Face Recognition: DA-DCT

by Nipun Behl, Shivangi Katiyar
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 141 - Number 11
Year of Publication: 2016
Authors: Nipun Behl, Shivangi Katiyar
10.5120/ijca2016909848

Nipun Behl, Shivangi Katiyar . Diagonally Assisted DCT Technique for Face Recognition: DA-DCT. International Journal of Computer Applications. 141, 11 ( May 2016), 11-15. DOI=10.5120/ijca2016909848

@article{ 10.5120/ijca2016909848,
author = { Nipun Behl, Shivangi Katiyar },
title = { Diagonally Assisted DCT Technique for Face Recognition: DA-DCT },
journal = { International Journal of Computer Applications },
issue_date = { May 2016 },
volume = { 141 },
number = { 11 },
month = { May },
year = { 2016 },
issn = { 0975-8887 },
pages = { 11-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume141/number11/24827-2016909848/ },
doi = { 10.5120/ijca2016909848 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:43:14.958816+05:30
%A Nipun Behl
%A Shivangi Katiyar
%T Diagonally Assisted DCT Technique for Face Recognition: DA-DCT
%J International Journal of Computer Applications
%@ 0975-8887
%V 141
%N 11
%P 11-15
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face is a multidimensional intricate structure that demands good computing technique for recalling it. This paper proposes a new diagonally assisted face recognition technique using Discrete Cosine Transform (DCT). Proposed work divides an image into matrix to apply DCT. Working scheme i.e. DA-DCT selects diagonal coefficient to retain low, moderate and high frequency coefficients for each block. Proficiency of the proposed work has been checked for self-created database using MATLAB. Experimental results prove that DA-DCT performs much better than PCA.

References
  1. G.M. Zafaruddin and H.S. Fadewar, “face recognition: a holistic approach review”, in proceeding of IEEE international conference on Contemporary Computing and informatics (IC3I), pp. 175-178, Nov-2014.
  2. Xiaolong Fan and Brijesh Verma, “Selection and fusion of facial features for face recognition” International journal of Expert systems with applications, pp. 7157-7169, Aug-2008.
  3. Stewart Tseng, “Comparison of Holistic and Feature based Approaches to Face recognition” Thesis report, July-2003.
  4. Vandana S. Bhat and Dr. Jagadeesh D. Pujari, “Face Recognition Using Holistic Based Approach”, International Journal of Emerging Technology and Advanced Engineering, pp. 1-8, vol. 4, July-2014.
  5. Mandeep Kaur, Rajeev Vashisht, Nirvair Neeru, “Recognition of Facial Expression with Principal Component Analysis and Singular Value Decomposition”, in International Journal of Computer Applications, vol.12, pp. 36-40, Nov-2010.
  6. Amirhosein Nabatchian, Esam Abdel-Raheem, Majid Ahmadi, “Illumination Invariant Feature Extraction and Mutual-Information-Based Local Matching for Face Recognition Under Illumintion Variation and Occlusion”, pp.2576-2587, March 2011.
  7. Hamidreza Rashidy Kanan and Karim Faez, “GA-based optimal selection of PZMI features for face recognition”, journal of Applied Mathematics and Computation, 706-715, 2008.
  8. P.N. Belhumeur, J.P. Hespanha, D.J. Kriegman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection”, in proceeding of IEEE Transaction, pp. 711-720, 1997.
  9. L. Pessoa, A.P. Leitao, “Complex cell prototype representation for face recognition”, in proceeding of IEEE Transaction, Neural Networks, pp. 1528-1531, 1999.
  10. Z.Q. Zhao, D.S. Huang, B.Y. Sun, “Human face recognition based on multi-features using neural networks committee”, Pattern Recognition, pp.1351–1358, 2004.
  11. L. Guo, D.S. Huang, “Human face recognition based on radial basis probabilistic neural network”, in: Proceedings of the International Joint Conference on Neural Networks (IJCNN 2003), pp. 2208–2211, 2003.
  12. W.S. Chen, P.C. Yuen, J. Huang, D.Q. Dai, “Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition”, IEEE Trans. Syst. Man Cybern. ,pp. 659-669, 2005.
  13. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, K.R. Müller, “Fisher discriminant analysis with kernels”, in: Proceedings of the IEEE Workshop on Neural Networks for Signal Processing IX, pp. 41–48, 1999.
  14. S. Mika, G. Rätsch, J. Weston, B. Schölkopf, A.J. Smola, K.R. Müller, “Invariant feature extraction and classification in feature spaces”, in: S.A. Solla, T.K. Leen, K.R. Müller (Eds.), Advances in Neural Information Processing Systems, vol. 12, Cambridge, pp. 526–532, 2000.
  15. L.F. Chen, H.Y. Liao, M.T. Ko, J.C. Lin, G.J. Yu, “A new LDA-based face recognition system which can solve the small sample size problem”, Pattern Recogn., pp.1713–1726, 2000.
  16. H. Yu, J. Yang, “A direct LDA algorithm for high-dimensional data-with application to face recognition”, Pattern Recogn., pp. 2067–2070, 2001.
  17. P.N. Belhumeur, J.P. Hespanha, D.J. Kriegman, “Eigenfaces vs. Fisherfaces: recognition using class specific linear projection”, IEEE Trans. Pattern Anal. Mach. Intell., pp.711–720, 1997.
  18. D.Q. Dai, P.C. Yuen, “Regularized discriminant analysis and its applications to face recognition”, Pattern Recogn. Pp. 845–847, 2003.
Index Terms

Computer Science
Information Sciences

Keywords

Discrete Cosine Transform Face recognition