Facial expressions recognition based on dimensionality reduction techniques
Abstract
Keywords
References
. Arnold. W. M. Smeulders, M. Worring, S. Satini, A. Gupta, R. Jain. Content – Based Image Retrieval at the end of the Early Years, IEEE Transactions on Pattern analysis and Machine Intelligence, Vol. 22, No. 12, pp 1349-1380 , 2000.
. Gupta. A. Visual Information Retrieval Technology: A Virage respective, Virage Image Engine. API Specification, 1997.
Ping-Cheng Hsieh,Pi-Cheng Tung,A Novel Hybrid Approach Based On Sub pattern Technique and Whitened PCA for Face Recognition, Pattern Recognition 42 (2009) 978-984.
Vytautas Perlibakas Distance measures for PCA based face recognition, Pattern Recognition letters 25 (2004) 711-724
S.I. Choi, C. Kim, C.H. Choi, Shadow compensation in 2D images for face recognition, Pattern Recognition 40 (2007) 2118–2125.
M. Turk, A. Pentland, Eigenfaces for recognition, J. Cognitive Neurosci.3 (1) (1991) 71–86.
Yalefacedatabase http://cvc.yale.edu/projects/yalefaces/yalefaces.html.
J. Yang, D. Zhang, J.Y. Yang, Is ICA significantly better than PCA for face recognition? in: Proceedings of IEEE International Conference on Computer Vision, vol. 1, 2005, pp. 198–203.
A.J. Bell, T.J. Sejnowski, The independent components of natural scenes are edge filters, Vision Res. 37 (23) (1997) 3327–3338.
M.S. Bartlett, H.M. Lades, T.J. Sejnowski, Independent component representation for face recognition, in: Proceedings of SPIE Symposium on Electronic Imaging: Science and Technology, 1998, pp. 528–539.
S. Chen, Y. Zhu, Subpattern-based principle component analysis, Pattern Recognition 37 (2004) 1081–1083.
K.Tan and S.Chen. Adaptively weighted sub-pattern pca for face recognition. Neurocomputing, 64:505–511, 2005.
R. Gottumukkal and V.K. Asari. An improved face recogntion
technique based on modular pca approach. Pattern Recogn.
Lett., 25(4):429–436, 2004.
Yang J and Zhang D. Two-dimensional pca:a new approach to
appearance-based face representation and recognition. IEEE
Transactions on Pattern Analysis and Machine Intelligence,
(1):131–137, 2004.
S. H. Ou, Q. L. Wang, and Z. Y. Zhu. The Application
and Technology of Digital Image Processing(In Chinese).
Tsinghua Press, Beijing, China, 2004.
Vytautas Perlibakas “distance measure for PCA-based face recognition. Pattern Recognition Letters 25 (2004) 711–724
Gupta Navarrete, P., Ruiz-del-Solar, J., 2001. Eigenspace-based recognition of faces: Comparisons and a new approach. In: International Conference on Image Analysis and Processing
ICIAP2001. pp. 42–47.
Graham, D.B., Allinson, G.N.M., 1998. Characterizing virtual
eigensignatures for general purpose face recognition. Face
recognition: From theory to applications. NATO ASI Series
F, Computer and Systems Sciences 163, 446–456.
Swets, D.L., Pathak, Y., Weng, J.J., 1998. An image database system with support for traditional alphanumeric queries and content-based queries by example. Multimedia Tools Appl. (7), 181–212.
Yilmaz, A., Gokmen, M., 2001. Eigenhill vs. eigenface and eigenedge. Pattern Recognition 34, 181–184.
D.Q. Zhang, S.C. Chen, J. Liu, Representing image matrices: eigenimages vs. eigenvectors, in: Proceedings ofthe Second International Symposium on Neural Networks (ISNN’05), Lecture Notes in Computer Science, vol. 3497, Chongqing, China, 2005, pp. 659–664.
Paul Computer and Ahmad , Afandi and Amira, Abbes ,optimal discrete wavelet transform (dwt) features for face recognition Nicholl, (2010), Malaysia
Refbacks
- There are currently no refbacks.
Copyright © 2013, All rights reserved.| ijseat.com
International Journal of Science Engineering and Advance Technology is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJSEat , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.