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Feature Generation Method for Low-Resolution Face Recognition
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 Title & Authors
Feature Generation Method for Low-Resolution Face Recognition
Choi, Sang-Il;
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 Abstract
We propose a feature generation method for low-resolution face recognition. For this, we first generate new features from the input features (pixels) of a low-resolution face image by adding the higher-order terms. Then, we evaluate the separability of both of the original input features and new features by computing the discriminant distance of each feature. Finally, new data sample used for recognition consists of the features with high separability. The experimental results for the FERET, CMU-PIE and Yale B databases show that the proposed method gives good recognition performance for low-resolution face images compared with other method.
 Keywords
Feature Generation;Feature Selection;Discriminant Distance;Low-resolution Face Recognition;
 Language
Korean
 Cited by
 References
1.
W. Zhao, R. Chellappa, P.J. Phillips, and A. Rosenfeld, “Face Recognition: A Literature Survey,” Journal of Association for Computing Machinery Computing Surveys, Vol. 35, No. 4, pp. 399-458, 2003.

2.
Y.H. Cho, "Face Recognition using First Moment of Image and Eigenvectors," Journal of Korea Multimedia Society, Vol. 9, No. 1, pp. 33-40, 2006.

3.
H.C. Lee, "A Face Recognition System using Geometric Image Processing," Journal of Korea Multimedia Society, Vol. 6, No. 7, pp. 1139-1148, 2003.

4.
M. Turk and A. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, Vol. 3, No. 1, pp. 71-86, 1991. crossref(new window)

5.
P.N. Belhumeur, J.P. Hespanha, and D.J. Kriegman, “Eigenfaces vs. Fisherfaces: Recognition using Class Specific Linear Projection,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 19, No. 7, pp. 711-720, 1997. crossref(new window)

6.
H. Cevikalp, M. Neamtu, M. Wilkes, and A. Barkana, “Discriminative Common Vectors for Face Recognition,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 27, No. 1, pp. 4-13, 2005. crossref(new window)

7.
S.I. Choi, N. Kwak, G.M. Jeong, and C.H. Choi, “Pixel Selection based on Discriminant Features with Application to Face Recognition,” Journal of Pattern Recognition Letters, Vol. 33, No. 9, pp. 1083-1092, 2012. crossref(new window)

8.
D. Zhou, X. Yang, N.S. Peng, and Y.H. Wang, “Improved-LDA Based Face Recognition using both Facial Global and Local Information,” Journal of Pattern Recognition Letters, Vol. 27 No. 6, pp. 536-543, 2006. crossref(new window)

9.
N. Kwak, S.I. Choi, and C.H. Choi, “Feature Extraction for Regression Problems and an Example Application for Pose Estimation of a Face,” Proceedings of the 5th International Conference on Image Analysis and Recognition, pp. 435-444, 2008.

10.
X. Jiang, B. Mandal, and A. Kot, “Eigenfeature Regularization and Extraction in Face Recognition,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 30, No. 3, pp. 1-12, 2008. crossref(new window)

11.
H.J. Moon and S.H. Kim, "Computational Analysis of PCA-based Face Recognition Algorithms," Journal of Korea Multimedia Society, Vol. 6, No. 2, pp. 247-258, 2003.

12.
S.I. Choi, J. Oh, C.H. Choi, and C. Kim, "Input Variable Selection for Feature Extraction in Classification Problems," Signal Processing, Vol. 92, No. 3, pp. 636-648, 2012. crossref(new window)

13.
R.O. Duda, P.E. Hart, and D.G. Stork. Pattern Classification. 2nd, Wiley Interscience, New York, 2001.

14.
K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd. Academic Press, San Diego, 1990.

15.
T.M. Cover, "Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition," IEEE Transactions on Electronic Computers, Vol. 14, No. 3, pp. 326-334, 1965. crossref(new window)

16.
C. Cortes and V. Vapnik, "Support-Vector Networks," Machine Learning, Vol. 20, No. 3, pp. 273-297, 1995.

17.
B. Scholkopft and K.R. Mullert. "Fisher Discriminant Analysis with Kernels," Proceedings of the 1999 IEEE Signal Processing Society Workshop Neural Networks for Signal Processing IX, pp. 23-25, 1999.

18.
J. Yang, A.F. Frangi, J. Yang, D. Zang, and Z. Jin, "KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 2, pp. 230-244, 2005. crossref(new window)

19.
J. Liang, S. Yang, and A. Winstanley, "Invariant Optimal Feature Selection: A Distance Discriminant and Feature Ranking Based Solution," Pattern Recognition, Vol. 41, No. 5, pp. 1429-1439, 2008. crossref(new window)

20.
The Color FERET Database, http://www.nist.gov/humanid/colorferet (accessed Apr., 20, 2015)

21.
T. Sim, S. Baker, and M. Bsat, “The CMU Pose, Illumination, and Expression Database,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 25, No. 12, pp. 1615-1618, 2003. crossref(new window)

22.
A.S. Georghiades and P.N. Belhumeur, “From Few to Many: Illumination Cone Models for Face Recognition Under Variable Lighting and Pose,” IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 23, No. 2, pp. 643-660, 2001. crossref(new window)

23.
R.B. Marimont and M.B. Shapiro, "Nearest Neighbour Searches and the Curse of Dimensionality," Journal of Institute of Mathematics and its Applications Applied Mathematics, Vol. 24, No. 1, pp. 59-70, 1979. crossref(new window)

24.
R.C. Gonzalez and R.E. Woods, Digital Image Processing, Prentice hall Upper Saddle River, New Jersey, 2002.

25.
R. Keys, "Cubic Convolution Interpolation for Digital Image Processing," IEEE Transactions on Acoustics, Speech, and Signal Processing, Vol. 29, No. 6, pp. 1153-1160, 1981. crossref(new window)