loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 3
Age Classification using Fuzzy Lattice Neural Network
Jinan, China
October 16-October 18
ISBN: 0-7695-2528-8
ASCII Text
x 
 
D. Kalamani, P. Balasubramanie, "Age Classification using Fuzzy Lattice Neural Network," Intelligent Systems Design and Applications, International Conference on, vol. 3, pp. 225-230, Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 3, 2006.
 
 
BibTex
x
 
@article{ 10.1109/ISDA.2006.8,
author = {D. Kalamani and P. Balasubramanie},
title = {Age Classification using Fuzzy Lattice Neural Network},
journal ={Intelligent Systems Design and Applications, International Conference on},
volume = {3},
year = {2006},
isbn = {0-7695-2528-8},
pages = {225-230},
doi = {http://doi.ieeecomputersociety.org/10.1109/ISDA.2006.8},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Intelligent Systems Design and Applications, International Conference on
TI - Age Classification using Fuzzy Lattice Neural Network
SN - 0-7695-2528-8
SP225
EP230
A1 - D. Kalamani,
A1 - P. Balasubramanie,
PY - 2006
KW - Adaptive Resonance Theory Networks
KW - Feature Extraction
KW - Fuzzy Lattice Neural model
KW - Image Classification.
VL - 3
JA - Intelligent Systems Design and Applications, International Conference on
ER -
 
D. Kalamani, Kongu Engineering College, India
P. Balasubramanie, Kongu Engineering College, India
This paper presents an age classification of a person from the gray scale facial images using Fuzzy Lattice Neural (FLN) model. The FLN model is a combination of fuzzy set theory, lattice theory and Adaptive Resonance Theory Neural model. The proposed system comprises of three sections, namely, location, feature extraction and age classification. From each facial image, three areas are located and three wrinkle features extracted from each location. The extracted nine (3x3) features are applied to FLN model. The FLN model trains the input and classifies the age of a person from the facial image. The proposed system is developed on MATLAB 6p1 and object oriented programming language C++. The success rate of the age classification is about 95% over Kwon and Lobo model and Wen, Chung and Chun model.
Index Terms:
Adaptive Resonance Theory Networks, Feature Extraction, Fuzzy Lattice Neural model , Image Classification.
Citation:
D. Kalamani, P. Balasubramanie, "Age Classification using Fuzzy Lattice Neural Network," isda, vol. 3, pp.225-230, Sixth International Conference on Intelligent Systems Design and Applications (ISDA'06) Volume 3, 2006
Usage of this product signifies your acceptance of the Terms of Use.