A new approach: the local feature extraction based on the new regulation of the locally preserving projection

Muntasa, Arif

doi:10.12988/ams.2015.55408

Cited by 5 publications

(5 citation statements)

References 18 publications

(16 reference statements)

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“…The calculation results of the S-LST as written in the Equation (5) The results of Equation (10) delivered the Eigenvalue and the Eigenvector, where the Eigenvalues are decreasingly ordered as mentioned in Equation (11) and followed the Eigenvectors shifting based on the corresponding column position to obtain the primary features as written in the Equation (12) m m…”

Section: Compute the New Spaces Of The S-lstmentioning

confidence: 99%

“…Many approaches have been promoted to overcome the problems, i.e. Covariance-based subspace [1], Eigenface [2][3][4], Fisherface [5][6][7][8][9], Independent Component Analysis [10], Local manifold model [11][12], Laplacian Smoothing Transform (LST) [13][14], Kernel subspace [15], Tangent Space [16], Sparse Neighborhood [17], Wavelet [18], Graph embedding [19], Matrix-based Features [20], Vertical and Horizontal Information [21], Locally Linear Regression [22], and Homogeneous and Non-homogeneous Polynomial Model [23].…”

Section: Introductionmentioning

confidence: 99%

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Features Fusion based on the Fisherface and Simplification of the Laplacian Smoothing Transform

Muntasa¹

2017

ijeei

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The newest model was proposed to extract the characteristic as distinctive attribute based on the Simplification of the Laplacian Smoothing Transform (S-LST) and the Fisherface. The proposed model is composed of two primary processes, i.e., training and testing processes. A training process involved features extraction based on the S-LST and the Fisherface, selection of the principal features, and features fusion of them. The proposed model has proved that the features can preserve the particular global information and the local feature areas of the rows and columns. The testing process is the stage to obtain the primary features through the new projection of the training sets. The last operation of the testing process is to measure the similarity of the features. The proposed model was tested using three different databases, i.e., the ORL, the YALE, and the UoB. The proposed model demonstrated the best performance, which is 100% recognition rate on the ORL and YALE when five images are implemented to the training sets. The maximum average of the recognition rate is 98.7% for the ORL database, 97.7% for the YALE database, and 94.53% for the UoB database. The comparison result showed that the proposed model performs better than the other approach, i.e., LST+LDA, S-MFA, S-NPE, S-LPP, S-LDA, LPP, LDA, and PCA.

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Section: Compute the New Spaces Of The S-lstmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Features Fusion based on the Fisherface and Simplification of the Laplacian Smoothing Transform

Muntasa¹

2017

ijeei

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“…The first process of Two-Dimensional Eigenfaces is to compute the average of the training sets as shown in Equation The result of Equation (9) will be applied to compute the zero mean and covariance. They can be written in Equation (10) and (11)…”

Section: Two-dimensional Eigenface Of the Ldmmentioning

confidence: 99%

“…Principally, dimensionality reduction is used to obtain the main features of an object, so that computation time of the similarity measurement can be significantly reduced. Many methods have been developed to overcome high dimensionality, such as the Principal Component Analysis wellknown as PCA [7][8], Linear Discriminant Analysis [7], Kernel PCA [9][10], Linear Preserving Projection or Laplacianfaces [11][12], Gabor Wavelet [13], and Two-Dimensional Fisherface [14][15]. One of the most common and the oldest methods to extract the features is PCA.…”

Section: Introductionmentioning

confidence: 99%

Maximum Value of the Symmetrical Diagonal Matrix Based Two - Dimensional Eigenface s for Facial Recognition on the Variant Lighting and Expressions

Muntasa

Telang²

2016

ijeei

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This research proposed a new approach to recognize face image under various lighting expression. The Proposed method is started by generating of the Left Diagonal Matrix (LDM) and the Right Diagonal Matrix (RDM). Subsequently, the dimensionality reduction is conducted by using Two-Dimensional Eigenface of the LDM and the RDM. The results of dimensionality reduction are selected the maximum value based on the corresponding features. Finally, the geometric similarity measurement model is carried out to obtain the recognition rate. The proposed method was evaluated using three different facial image databases, which are the YALE, the ORL and the UoB face image databases. Two until six column features were used to measure the similarity between the training and the testing sets. Experimental results revealed that the proposed method produced 92.2%, 94%, and 94.7% recognition rate for the YALE, the ORL, and the UoB face image databases respectively. The proposed method was also compared to the other methods. On the ORL face image database, the comparison results show that the proposed method outperformed to the Eigenface, the Fisherface, and the Laplacianfaces but not for O-Laplacianfaces and Two-Dimensional Principal Component Analysis method. On the YALE and the UoB face image databases, the proposed method outperformed to the other appearance methods which are the Eigenface, the Fisherface, the Laplacianfaces, the O-Laplacianfaces and Two-Dimensional Principal Component Analysis methods.In this research, Selection of the maximum values of the left and the right of the symmetrical diagonal matrix was proposed. To obtain the main features and to reduce the dimension and computation time, Two-Dimensional Principal Component Analysis was proposed to extract it. The proposed method ensured the features applied are the maximum value of the dominant features of the corresponding features.The rest of this paper was organized into four sections. the second section explains the weakness of one-dimensional of appearance methods. The third part presents the proposed method. The series of the proposed method are also presented in this part, which are the Left Diagonal Matrix or LDM, the Right Diagonal Matrix or RDM, Two-Dimensional Eigenface of the LDM, Two-Dimensional Eigenface of the RDM¸ selection of the maximum value of the LDM and the RDM features, and similarity measurement. Fourthly, experimental results are explained and followed by analysis. Finally, conclusions are written In the fifth section. The Weakness of One-Dimensional of Appearance Methods.In the biometrics field, there are three method models to extract the main features of an object, i.e. appearance-based, features-based, and hybrid-based. An appearance-based method extracted an object based on its vision. An object is viewed as space that consists of the features. Two crucial problems on an appearance-based method, which are high dimensional measured and computational time consumed. Principal Component Analysis (PCA or 1D-PCA) is the simplest method based o...

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“…Many algorithms have been implemented for dimensionality reduction, which are based on appearance (Shih et al, 2008;Wright et al, 2009;Martinez and Kak, 2001;Yang et al, 2004;Li and Yuan, 2005;Muntasa, 2014;2015a;2015b;Zhang and Zha, 2004;Sanguinetti, 2008), geometrical (Muntasa et al, 2012;Rizvandi et al, 2007;Cootes et al, 2000) and hybrid (Cootes et al, 2000;Tang and Wang, 2003).…”

Section: Introductionmentioning

confidence: 99%

The Human Facial Expression Classification Using the Center Kernel Subspace based the Ridge Regression

Muntasa¹

2015

Journal of Computer Science

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The facial expression classification has been implemented on many devices. However, many researchers have conducted the research to improve the classification rate. This research has developed the algorithm to enhance the classification rate on the facial expression field. The proposed method is divided into five primary processes, which are, the first, create the center kernel subspace-based the ridge regression. Secondly, create five scales and eight orientations by using Gabor Filter Bank. The third is to obtain the new signal by using Two-dimensional-Fast Fourier Transform. Fourth, the results are used to build the feature space. It is conducted by the ridge regression of center kernel function. The last process, the primary features can be generated by multiplication between the center kernel and the Eigenvalue. The expression classification can be obtained by using the Mahalanobis method. The proposed method has been evaluated on JAFEE facial expression image database. Experimental shows that the classification rates for the first until the last scenarios are 83.33, 84.03, 86.61, 87.23, 87.24 and 89.79% respectively.

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A new approach: the local feature extraction based on the new regulation of the locally preserving projection

Cited by 5 publications

References 18 publications

Features Fusion based on the Fisherface and Simplification of the Laplacian Smoothing Transform

Features Fusion based on the Fisherface and Simplification of the Laplacian Smoothing Transform

Maximum Value of the Symmetrical Diagonal Matrix Based Two - Dimensional Eigenface s for Facial Recognition on the Variant Lighting and Expressions

The Human Facial Expression Classification Using the Center Kernel Subspace based the Ridge Regression

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