Face recognition by generalized two-dimensional FLD method and multi-class support vector machines

Chowdhury, Shiladitya; Sing, Jamuna Kanta; Basu, Dipak Kumar; Nasipuri, Mita

doi:10.1016/j.asoc.2010.12.002

Cited by 44 publications

(27 citation statements)

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“…7, No. 6, December 2017 223 doi: 10.18178/ijmlc.2017.7.6.651 among the classes while minimizing it within a class [9]. Recently, researchers have devised different fuzzy-based techniques e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Two-dimensional FLD (2DFLD) method also works directly on the 2D image matrices and maximizes class separability either from row or column direction [8]. The 2DFLD method is much superior in comparison to PCA and 2DPCA methods in terms of feature extraction and face recognition [9]. An improvement of the 2DFLD method, the generalized 2DFLD (G-2DFLD) method brings out the projection vectors both from the row and column directions from the training images [9].…”

Section: Introductionmentioning

confidence: 99%

“…The 2DFLD method is much superior in comparison to PCA and 2DPCA methods in terms of feature extraction and face recognition [9]. An improvement of the 2DFLD method, the generalized 2DFLD (G-2DFLD) method brings out the projection vectors both from the row and column directions from the training images [9]. Then, linearly projecting an image matrix on these row and column projection vectors the discriminant feature vectors are obtained in the form of a matrix.…”

Section: Introductionmentioning

confidence: 99%

“…methods can be classified into three main categories: (a) holistic approach, (b) feature based approach and (c) hybrid approach [1]- [9].…”

Section: Introductionmentioning

confidence: 99%

“…Broadly, the face recognition Manuscript received August 21, 2017; revised November 10, 2017. Aniruddha Dey and Jamuna Kanta Sing are with the Department of Computer Science Engineering, Jadavpur University, Kolkata, jksing@ieee.org).Shiladitya Chowdhury is with the Department of Computer Application, Techno India, Kolkata, India (e-mail: dityashila@yahoo.com).methods can be classified into three main categories: (a) holistic approach, (b) feature based approach and (c) hybrid approach [1]- [9].Holistic approach: This approach uses all the information available in the face images as a whole. The subspace-based methods, like principal component analysis (PCA), linear discriminant analysis (LDA) or Fisher"s linear discriminant (FLD) [2], [3] and their variants, which use eigenfaces and/or fisherface to compute features, fall under this category.…”

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

Dey¹,

Sing²,

Chowdhury³

2017

IJMLC

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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. I. INTRODUCTIONIn the last few decades the pattern recognition, biometrics and feature extraction became very popular research area among the researchers [1]- [4]. In this context, the face recognition problem can be viewed as a classification problem where one or more test images are compared with the face images stored in the databases. Although the face recognition techniques from still images having controlled background have matured substantially, the face recognition task is still very challenging under uncontrolled environment and different issues related to human face i.e. facial expression (unhappiness, happiness) and facial pose, occlusion (wearing beards, mustaches and glasses), illumination variance, etc. Broadly, the face recognition Manuscript received August 21, 2017; revised November 10, 2017. Aniruddha Dey and Jamuna Kanta Sing are with the Department of Computer Science Engineering, Jadavpur University, Kolkata, jksing@ieee.org).Shiladitya Chowdhury is with the Department of Computer Application, Techno India, Kolkata, India (e-mail: dityashila@yahoo.com).methods can be classified into three main categories: (a) holistic approach, (b) feature based approach and (c) hybrid approach [1]- [9].Holistic approach: This approach uses all the information available in the face images as a whole. The subspace-based methods, like principal component analysis (PCA), l...

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Section: Introductionmentioning

confidence: 99%