Feature extraction and classification using deep convolutional neural networks, PCA and SVC for face recognition

Benkaddour, Mohammed Kamel; Bounoua, Abdennacer

doi:10.3166/ts.34.77-91

Cited by 28 publications

(27 citation statements)

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“…In recent years, many scholars have probed into small sample identification. For instance, Benkaddour and Bounoua [17] conducted feature extracted with deep convolutional neural network (DCNN) and completed face recognition by the PCA and support vector classifier (SVC). Reddy et al [18] suggested recognizing facial emotions with nonlinear principal component analysis (NLPCA) and support vector machine (SVM).…”

Section: Discussionmentioning

confidence: 99%

A Neighborhood Structure-Preserving Bi-objective Optimization Method Based on Class Center and Discriminant Analysis and Its Application in Facial Recognition

Ma¹

2019

RIA

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Based on class center and discriminant analysis, this paper puts forward a novel bi-objective optimization method that preserves the neighborhood structure, and applies it to facial recognition. Firstly, the locally preserving projection (LPP) was improved into the class-center locally preserving projection (CLPP) by replacing the sample-based neighborhood structure with the class center-based neighborhood structure. Next, a bi-objective optimization model was developed based on the CLPP and linear discriminant analysis (LDA), and solved by the multi-objective optimization theory. The bi-objective optimization problem combines the merits of single-target CLPP and single-target LDA: the class center-based neighborhood structure is preserved, and the class information is introduced naturally, making up for the defect of the LDA due to the manual changes of adjacency coefficient and highlighting the physical meaning. Finally, several experiments were conducted on AR, CAS-60 and FERET face databases. The experimental results prove that our methods are correct and effective, and the bi-objective optimization method based on CLPP and LDA (CLPP+LDA) achieved the best recognition effect.

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Section: Discussionmentioning

confidence: 99%

A Neighborhood Structure-Preserving Bi-objective Optimization Method Based on Class Center and Discriminant Analysis and Its Application in Facial Recognition

Ma¹

2019

RIA

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“…As shown in Figure 5, least squares boosting (LSBoost) is a sequential ensemble method that sequentially builds a decision tree. It works in a way that compensates for errors in the previous tree and is defined as shown in Equation 7 [29,30].…”

Section: Ensemble Methodsmentioning

confidence: 99%

Study on Prediction and Application of Initial Chord Elastic Modulus with Resonance Frequency Test of ASTM C 215

Yoon

Choi

2020

Applied Sciences

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For accurate design, construction, and maintenance, it is important to identify the elastic modulus of concrete. This is usually achieved using a destructive test based on American Society for Testing and Materials (ASTM) C469. However, obtaining an appropriate static elastic modulus (Ec) requires many specimens, and the testing is difficult and time-consuming. Thus, a dynamic elastic modulus (Ed) is often obtained through a natural frequency for a specific size (e.g., the longitudinal (LT) or transverse (TR) mode) based on a resonance frequency test. However, this method uses a gradient at a very low-stress part of the stress–strain curve and assumes a completely elastic body. In fact, the initial chord elastic modulus (Ei) of the stress–strain curve in a concrete fracture test differs from the Ed, owing to the non-homogeneity and inelasticity of the concrete. The Ei of the experimental value may be more accurate. In this study, the Ei was predicted using machine learning methods for natural frequencies. The prediction accuracy for Ei was analyzed based on f1–f4, as calculated through the LT and TR modes. The predicted Ei had higher correlations with the actual Ec and compressive strength (fc) than Ed. Thus, more accurate prediction of concrete mechanical properties is possible.

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“…However, the L1 regularized SVM method can be less efficient in data analysis as the number of features that are automatically selected increases. Therefore, in this study, the PCA-SVM method, which can easily manage key factors by filtering a high amount of data in advance and has low computational effort and cost, was used [31].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Damage Evaluation of Porcelain Insulators with 154 kV Transmission Lines by Various Support Vector Machine (SVM) and Ensemble Methods Using Frequency Response Data

et al. 2019

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Frequency response signals have been used for the non-destructive evaluation of many different structures and for the integrity evaluation of porcelain insulators. However, it is difficult to accurately estimate the integrity of porcelain insulators under various environmental conditions only by using general frequency response signals. Therefore, this study used a method that extracted several features that can be derived from the frequency response signal and reduced their dimensions to select features suitable for the evaluation of the soundness of porcelain insulators. The latest machine learning techniques were used to identify correlations and not for basic feature analyses. Two machine learning models were developed using the support vector machine and ensemble methods in MATLAB. Both models showed high reliability in distinguishing between normal and defective porcelain insulators, and they could visualize the distribution area of the data by extracting quantitative values and applying machine learning, rather than simply verifying the frequency response signal.

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Feature extraction and classification using deep convolutional neural networks, PCA and SVC for face recognition

Cited by 28 publications

References 0 publications

A Neighborhood Structure-Preserving Bi-objective Optimization Method Based on Class Center and Discriminant Analysis and Its Application in Facial Recognition

A Neighborhood Structure-Preserving Bi-objective Optimization Method Based on Class Center and Discriminant Analysis and Its Application in Facial Recognition

Study on Prediction and Application of Initial Chord Elastic Modulus with Resonance Frequency Test of ASTM C 215

Damage Evaluation of Porcelain Insulators with 154 kV Transmission Lines by Various Support Vector Machine (SVM) and Ensemble Methods Using Frequency Response Data

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