Human Thermal Face Recognition Based on Random Linear Oracle (RLO) Ensembles

Gaber, Tarek; Tharwat, Alaa; Ibrahim, Abdelhameed; Snáel, Václav; Hassanien, Aboul Ella

doi:10.1109/incos.2015.67

Cited by 21 publications

(13 citation statements)

References 20 publications

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“…The LDA technique and its variants have been applied in this application. For example, in [83,10,41,75,20,68], the LDA technique have been applied on face recognition. Moreover, the LDA technique was used in Ear [84], fingerprint [44], gait [5], and speech [24] applications.…”

Section: Biometrics Applicationsmentioning

confidence: 99%

Linear discriminant analysis: A detailed tutorial

Tharwat

Gaber

Ibrahim

et al. 2017

AIC

Self Cite

734

308

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Linear Discriminant Analysis (LDA) is a very common technique for dimensionality reduction problems as a preprocessing step for machine learning and pattern classification applications. At the same time, it is usually used as a black box, but (sometimes) not well understood. The aim of this paper is to build a solid intuition for what is LDA, and how LDA works, thus enabling readers of all levels be able to get a better understanding of the LDA and to know how to apply this technique in different applications. The paper first gave the basic definitions and steps of how LDA technique works supported with visual explanations of these steps. Moreover, the two methods of computing the LDA space, i.e. class-dependent and class-independent methods, were explained in details. Then, in a step-by-step approach, two numerical examples are demonstrated to show how the LDA space can be calculated in case of the class-dependent and class-independent methods. Furthermore, two of the most common LDA problems (i.e. Small Sample Size (SSS) and non-linearity problems) were highlighted and illustrated, and state-of-the-art solutions to these problems were investigated and explained. Finally, a number of experiments was conducted with different datasets to (1) investigate the effect of the eigenvectors that used in the LDA space on the robustness of the extracted feature for the classification accuracy, and (2) to show when the SSS problem occurs and how it can be addressed.

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Section: Biometrics Applicationsmentioning

confidence: 99%

Linear discriminant analysis: A detailed tutorial

Tharwat

Gaber

Ibrahim

et al. 2017

AIC

Self Cite

734

308

View full text Add to dashboard Cite

show abstract

“…Compared with some of the related work which used Terravic dataset, our proposed model achieved promising results (approximately 99%) while the model that were proposed in [4], [5] and [8] achieved 92.2%-94.1%, 93% and 94.1%, respectively. This achievement was obtained due to: (1) the proposed segmentation method, which extracts only the face and removes the background or any other noise, (2) using SFTA algorithm which extracts discriminative features, (3) using the rough set-based feature selection methods which remove the irrelevant features and improve the classification accuracy and (4) using the AdaBoost classifier which increases the weight of critical samples and hence improves the classification performance.…”

Section: Different Numbers Of Training Imagesmentioning

confidence: 77%

“…Gaber et al proposed a human thermal face recognition model which used the Segmentation-based Fractal Texture Analysis (SFTA) algorithm to extract texture features and then the Random Linear Oracle ensembles to identify the human face after applying two different dimensionality reduction techniques, namely, Linear Discriminant Analysis (LDA) [6] and PCA [7]. The experimental results proved that LDA-based approach was more efficient than PCA-based one and the best accuracy rate achieved was 94.12% using the Terravic Facial IR dataset [8].…”

Section: Optimized Superpixel and Adaboost Classifier For Human Thermal Face Recognitionmentioning

confidence: 99%

Optimized superpixel and AdaBoost classifier for human thermal face recognition

Ibrahim

Tharwat

Gaber

et al. 2017

SIViP

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“…On various optimization issues, such as temperature prediction, battery storage optimization, and leukemia detection [8], optimization algorithms have improved performance [9][10][11]. Electronics [12], informatics [13], energy [14][15][16], health [17], and many more disciplines of business [18][19][20][21] and research are among the numerous real-world applications [22][23][24][25][26][27].…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting of Appliances House in a Low-Energy Depend on Grey Wolf Optimizer

Zaini¹

2022

Computers, Materials &Amp; Continua

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This paper gives and analyses data-driven prediction models for the energy usage of appliances. Data utilized include readings of temperature and humidity sensors from a wireless network. The building envelope is meant to minimize energy demand or the energy required to power the house independent of the appliance and mechanical system efficiency. Approximating a mapping function between the input variables and the continuous output variable is the work of regression. The paper discusses the forecasting framework FOPF (Feature Optimization Prediction Framework), which includes feature selection optimization: by removing non-predictive parameters to choose the best-selected feature hybrid optimization technique has been approached. k-nearest neighbors (KNN) Ensemble Prediction Models for the data of the energy use of appliances have been tested against some bases machine learning algorithms. The comparison study showed the powerful, best accuracy and lowest error of KNN with RMSE = 0.0078. Finally, the suggested ensemble model's performance is assessed using a one-way analysis of variance (ANOVA) test and the Wilcoxon Signed Rank Test. (Two-tailed P-value: 0.0001).

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Human Thermal Face Recognition Based on Random Linear Oracle (RLO) Ensembles

Cited by 21 publications

References 20 publications

Linear discriminant analysis: A detailed tutorial

Linear discriminant analysis: A detailed tutorial

Optimized superpixel and AdaBoost classifier for human thermal face recognition

Forecasting of Appliances House in a Low-Energy Depend on Grey Wolf Optimizer

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