• Dec 20, 2017 News!ACMLC 2017 has been successfully held in NEC, Singapore during December 8-10.   [Click]
  • Dec 12, 2017 News!Good News! All papers from Volume 7, Number 1 to Volume 7, Number 5 have been indexed by Scopus!   [Click]
  • Dec 26, 2017 News!Vol.7, No.6 has been published with online version.   [Click]
Search
General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2017 Vol.7(6): 223-231 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2017.7.6.651

Weighted Fuzzy Generalized 2DFLD: A Fuzzy-Based Feature Extraction Technique for Face Recognition

Aniruddha Dey, Jamuna Kanta Sing, and Shiladitya Chowdhury
Abstract—This paper proposes a weighted scheme and fuzzy logic-based feature extraction technique, called weighted fuzzy generalized two-dimensional Fisher’s linear discriminant (WFG-2DFLD) and its use for face recognition using radial basis function (RBF) neural network as a classifier. In particular, the WFG-2DFLD method is extended version of the generalized two-dimensional Fisher’s linear discriminant (G-2DFLD) method. Like G-2DFLD, WFG-2DFLD also maximizes class separability along row and column directions simultaneously. Firstly, it calculates fuzzy membership matrix by fuzzy k-nearest neighbour (Fk-NN) algorithm for the training samples. Secondly, the fuzzy membership values are combined with the training samples to obtained global mean and class-wise mean training images. Thereafter, the global and class-wise mean images are used to generate fuzzy within-class and fuzzy between-class scatter matrices along the row and column directions. In order to make more accurate for classification, different weights are incorporated to scatter matrices. Finally, by solving the Eigen value problems of these scatter matrices; we find the optimal fuzzy projection vectors, which actually used to generate more discriminant features for face recognition. The WFG-2DFLD method has been evaluated on the YALE, AT&T (formally known as ORL), UMIST and FERET face databases using RBF neural network. Simulation results demonstrate that the proposed WFG-2DFLD method can obtain higher recognition rates than some state-of-the-art face recognition methods.

Index Terms—WFG-2DFLD, fuzzy projection vector, Fk-NN, RBFNN based classifier, matrix-based feature extraction.

Aniruddha Dey and Jamuna Kanta Sing are with the Department of Computer Science Engineering, Jadavpur University, Kolkata, India (e-mail: anidey007@gmail.com, jksing@ieee.org).
Shiladitya Chowdhury is with the Department of Computer Application, Techno India, Kolkata, India (e-mail: dityashila@yahoo.com).

[PDF]

Cite: Aniruddha Dey, Jamuna Kanta Sing, and Shiladitya Chowdhury, "Weighted Fuzzy Generalized 2DFLD: A Fuzzy-Based Feature Extraction Technique for Face Recognition," International Journal of Machine Learning and Computing vol. 7, no. 6, pp. 223-231, 2017.

Copyright © 2008-2015. International Journal of Machine Learning and Computing. All rights reserved.
E-mail: ijmlc@ejournal.net