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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
- 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.
- 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.
- Stewart Tseng, “Comparison of Holistic and Feature based Approaches to Face recognition” Thesis report, July-2003.
- 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.
- 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.
- 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.
- 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.
- 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.
- L. Pessoa, A.P. Leitao, “Complex cell prototype representation for face recognition”, in proceeding of IEEE Transaction, Neural Networks, pp. 1528-1531, 1999.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- H. Yu, J. Yang, “A direct LDA algorithm for high-dimensional data-with application to face recognition”, Pattern Recogn., pp. 2067–2070, 2001.
- 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.
- D.Q. Dai, P.C. Yuen, “Regularized discriminant analysis and its applications to face recognition”, Pattern Recogn. Pp. 845–847, 2003.
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