Face Recognition Using Diagonal 2D Linear Discriminant Analysis

Alsaqre, Falah; Ruan, Qiuqi; Yuan, Baozong; Tan, Zhenhui

doi:10.1109/icosp.2006.345749

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…The difference from D2D-FDA algorithm is that Dia2D-LDA algorithm uses diagonal rearrangement matrix of images all the time. More details of the Dia2D-LDA algorithm are introduced in Reference [16].…”

Section: B Diagonal 2dlda (Dia-2dlda)mentioning

confidence: 99%

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Tri-Direction 2D-Fisher Discriminant Analysis (T2D-FDA) for Feature Extraction

et al. 2019

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A new image feature extraction method for face recognition called Tri-direction 2D-Fisher Discriminant Analysis (T2D-FDA) is proposed to deal with the Small Sample Size (SSS) problem in conventional 1D-Fisher Discriminant Analysis (1D-FDA). Moreover, the essence of T2D-FDA is investigated, and the equivalence of the left-multiplying 2D-FDA of the original image matrices and the left-multiplying D2D-FDA of diagonal image matrices is verified if each column is viewed as a computational unit. Different from the 1D-LDA based approaches, the T2D-FDA is based on 2D image matrices rather than column vectors, so the image matrix does not need to be transformed into a long vector before feature extraction. The advantage arising in this way is that the SSS problem does not exist anymore because within-class scatter matrices constructed is full-rank in T2D-FDA. The proposed method is applied to face recognition where only few training images exist for each subject. Experiment results show T2D-FDA outperforms the current linear subspace methods in SSS problem on four public databases: ORL, Yale, AR and FERET face databases. INDEX TERMS Small sample size (SSS) problem, 2D-fisher discriminant analysis (2D-FDA), face recognition, within-class scatter matrices, linear subspace methods.

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Section: B Diagonal 2dlda (Dia-2dlda)mentioning

confidence: 99%

“…Kong et al [15] proposed unilateral 2D-FDA (U2D-FDA) and bilateral 2D-FDA (B2D-FDA). Alsaqre et al [16] proposed the Dia2D-LDA algorithm. The algorithm was similar with Dia2D-PCA algorithm proposed by Zhang et al [17].…”

Section: Introductionmentioning

confidence: 99%

Tri-Direction 2D-Fisher Discriminant Analysis (T2D-FDA) for Feature Extraction

et al. 2019

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“…또한, 특징 행렬의 크기를 줄 이기 위해서 Dia2DLDA에 2DLDA를 결합하였다. Alsaqre 등은 Dia2DPCA에서 얻은 투영 행렬을 이용하여 차원을 줄인 후 Dia2DLDA를 적용한 방법을 제안하였다 [14]. …”

Section: 대각 2dlda(dia2dlda)unclassified

Bilateral Diagonal 2DLDA Method for Human Face Recognition

Kim¹,

Song²,

Kim³

et al. 2009

Journal of Korean Institute of Intelligent Systems

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In this paper, a method called bilateral diagonal 2DLDA is proposed for face recognition. Two methods called Dia2DPCA and Dia2DLDA were suggested to reserve the correlations between the variations in the rows and columns of diagonal images. However, these methods work in the row direction of these images. A row-directional projection matrix can be obtained by calculating the between-class and within-class covariance matrices making an allowance for the column variation of alternative diagonal face images. In addition, column-directional projection matrix can be obtained by calculating the between-class and within-class covariance matrices making an allowance for the row variation in diagonal images. A bilateral projection scheme was applied using left and right multiplying projection matrices. As a result, the dimension of the feature matrix and computation time can be reduced. Experiments carried out on an ORL face database show that the proposed method with three different distance measures, namely, Frobenius, Yang and AMD, is more accurate than some methods, such as 2DPCA, B2DPCA, 2DLDA, etc.

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Face Recognition Using Diagonal Two-Dimensional Linear Discriminant Analysis and Wavelet Transform

Alsaqre

Alfarras

2017

2017 9th IEEE-GCC Conference and Exhibition (GCCCE)

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Face Recognition Using Diagonal 2D Linear Discriminant Analysis

Cited by 3 publications

References 14 publications

Tri-Direction 2D-Fisher Discriminant Analysis (T2D-FDA) for Feature Extraction

Tri-Direction 2D-Fisher Discriminant Analysis (T2D-FDA) for Feature Extraction

Bilateral Diagonal 2DLDA Method for Human Face Recognition

Face Recognition Using Diagonal Two-Dimensional Linear Discriminant Analysis and Wavelet Transform

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