Home   >   CSC-OpenAccess Library   >    Manuscript Information
Full Text Available

(187.44KB)
This is an Open Access publication published under CSC-OpenAccess Policy.
Publications from CSC-OpenAccess Library are being accessed from over 74 countries worldwide.
New Approach: Dominant and Additional Features Selection Based on Two Dimensional-Discrete Cosine Transform for Face Sketch Recognition
Arif Muntasa
Pages - 368 - 376     |    Revised - 30-08-2010     |    Published - 30-10-2010
Volume - 4   Issue - 4    |    Publication Date - October 2010  Table of Contents
MORE INFORMATION
KEYWORDS
Face sketch, one frequency, new dimension, dominant and additional features selection.
ABSTRACT
Modality reduction by using the Eigentransform method can not efficiently work, when number of training sets larger than image dimension. While modality reduction by using the first derivative negative followed by feature extraction using Two Dimensional Discrete Cosine Transform has limitation, which is feature extraction achieved of face sketch feature is included non-dominant features. We propose to select the image region that contains the dominant features. For each region that contains dominant features will be extracted one frequency by using Two Dimensional-Discrete Cosine Transform. To reduce modality between photographs as training set and face sketches as testing set, we propose to bring the training and testing set toward new dimension by using the first derivative followed by negative process. In order to improve final result on the new dimension, it is necessary to add the testing set pixels by using the difference of photograph average values as training sets and the corresponding face sketches average as testing sets. We employed 100 face sketches as testing and 100 photographs as training set. Experimental results show that maximum recognition is 93%.
CITED BY (2)  
1 Muntasa, a., sophan, m. k., hariadi, m., purnomo, m. h., & kondo, k. (2013). a new modeling of the landmark movement based on the previous movement results to detect the facial sketch features. journal of theoretical & applied information technology, 50(2).
2 Osman, S. M., Selim, G., & Salama, G. I. Photo-To-Sketch Matching Using Gabor Wavelet Transform.
1 Google Scholar 
2 CiteSeerX 
3 iSEEK 
4 Socol@r  
5 Scribd 
6 SlideShare 
7 PDFCAST 
8 PdfSR 
1 A. Muntasa, M. Hariadi., M. H. Purnomo. "Maximum Feature Value Selection Of Nonlinear Function Based On Kernel Pca For Face Recognition". In Proceeding of The 4th Conference On Information & Communication Technology and Systems, Surabaya, Indonesia, 2008
2 A. Muntasa, M. Hariadi., M. H. Purnomo. “A New Formulation of Face Sketch Multiple Features Detection Using Pyramid Parameter Model and Simultaneously Landmark Movement”. IJCSNS International Journal of Computer Science and Network Security, 9(9): 2009
3 A. Muntasa. “A Novel Approach for Face Sketch Recognition Based on the First Derivative Negative and 2D-DCT with Overlapping Model”. International Journal of Computer Science, (Accepted), 2010
4 D. Cristinacce and T.F. Cootes. "Facial Feature Detection using ADABOOST with Shape Constraints". In Proceeding .BMVC, Vol.1, 2000
5 Lu J., P. K.N. , V. A.N.. “Face Recognition Using Kernel Direct Discriminant Analysis Algorithms”. IEEE Trans. Neural Networks, 14(1):117-126, 2003
6 Su, H., Feng D., and Zhao R.-C. “Face Recognition Using Multi-feature and Radial Basis Function Network”. In Proceeding of the Pan-Sydney Area Workshop on Visual Information Processing, Sydney, Australia, 2002
7 X. He, S. Yan, Y. Hu, P. Niyogi, Hong-Jiang Zhang. "Face Recognition Using Laplacianfaces". IEEE Transactions On Pattern Analysis And Machine Intelligence,27(3):328-340, 2005
8 L. Wiskott, J.M. Fellous, N. Kruger, C. von der Malsburg. "Face Recognition by Elastic Bunch Graph Matching". IEEE Trans. on Pattern Analysis and Machine Intelligence, 19(7): 775- 779, 1997
9 A. Muntasa, M. Hariadi., M. H. Purnomo. "Maximum Feature Value Selection Of Nonlinear Function Based On Kernel Pca For Face Recognition". In Proceeding of The 4th Conferrence On Information & Communication Technology and Systems, Surabaya, Indonesia, 2008
10 M. Turk, A. Pentland. “Eigenfaces for recognition”. Journal of Cognitive Science, 71–86, 1991
11 J.H.P.N. Belhumeur, D. Kriegman. “Eigenfaces vs. fisherfaces: Recognition using class specific linear projection” IEEE Trans. on PAMI, 19(7):711–720, 1997
12 Yambor, W.S. “Analysis of PCA-Based and Fisher Discriminant-Based Image Recognition Algorithms”. Tesis of Master, Colorado State University, 2000
13 Bartlett, M. S., Movellan, J. R., & Sejnowski, T. J. “Face recognition by independent component analysis”. IEEE Trans. on Neural Networks, 13(6): 1450-1464, 2002
14 R.G. Uhl and N.d.V. Lobo. "A Framework for Recognizing a Facial Image from A Police Sketch". In Proceedings of CVPR, 1996
15 X. Tang, X. Wang. “Face Sketch Recognition”. IEEE Transactions on Circuits and Systems for Video Technology, 14(1):50-57, 2000
Dr. Arif Muntasa
Trunojoyo University - Indonesia
arifmuntasa@trunojoyo.ac.id