Using Hand-Dorsal Images to Reproduce Face Images by Applying Back propagation and Cascade-Forward Neural Networks

Al-Nima, Raıd Rafı Omar; Abdulraheem, Farqad H.; Al-Ridha, Moatasem Yaseen

doi:10.1109/iceccpce46549.2019.203755

Cited by 8 publications

(6 citation statements)

References 18 publications

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“…17, No. 6, December 2019: 3110-3119 3118 in [7,13] is that both of these works concentrated on regenerating full details of face images. In addition, all of the reported works in Table 1 have used the ORL face images database.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, all of the reported works in Table 1 have used the ORL face images database. From this table it can be seen that simple statistics were used with ANN techniques in [7,13], where the system strength level of the proposed systems is high. In this study, two fusion methods and two multi-spectral hand images (right and left) have been employed to regenerate full face details.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the strength level is very-high as spoofing the suggested system is so difficult. The EER values have been recorded to 10% for [7] and 6.43% and 2.86% for [13]. In this proposed approach, the EER value has been recorded equal to (1.99%).…”

Section: Discussionmentioning

confidence: 99%

“…Mai et al illustrates a Neighborly de-convolutional neural Network (NbNet) method to reproduce face images from deep templates [12]. Al-Nima et al exploited hand-dorsal images to generate full face details by using Back Propagation Neural (BPN) and Cascade-Forward Neural (CFN) Networks [13]. Up to now, it has been noted that there is no reference in relation to reproduce face images from multi-spectral hand images.…”

Section: Introductionmentioning

confidence: 99%

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Regenerating face images from multi-spectral palm images using multiple fusion methods

2019

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This paper established a relationship between multi-spectral palm images and a face image based on multiple fusion methods. The first fusion method to be considered is a feature extraction between different multi-spectral palm images, where multi-spectral CASIA database was used. The second fusion method to be considered is a score fusion between two parts of an output face image. Our method suggests that both right and left hands are used, and that each hand aims to produce a significant part of a face image by using a Multi-Layer Perceptron (MLP) network. This will lead to the second fusion part to reconstruct the full-face image, in order to examine its appearance. This topology provided interesting results of Equal Error Rate (EER) equal to 1.99%.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Regenerating face images from multi-spectral palm images using multiple fusion methods

2019

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“…GCNN is a novel architecture that influences the power of genetic algorithms to evolve and optimize neural network structures, allowing for the automatic discovery of complex and efficient network architectures (Figure 1). This approach holds promise for addressing the challenges of feature selection, network design, and hyperparameter tuning, which are often labor-intensive and time-consuming processes in traditional neural network development [11]. By integrating the principles of evolution and selection, GCNNs have the potential to revolutionize the field, providing efficient and customized solutions for a wide range of tasks, from pattern recognition to predictive modeling, and ultimately advancing the frontiers of artificial intelligence.…”

Section: Proposed Genetic Cascade-forward Neural Networkmentioning

confidence: 99%

Modeling of Dispersed Red 17 Dye Removal from Aqueous Solution Using Artificial Neural Network

Ibrahim,

Asmel,

Alabdraba

et al. 2024

J. Ecol. Eng.

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A significant amount of hazardous compounds has leaked into the environment due to the widespread usage of organic dyes, and it is essential that these dangerous contaminants be removed in a sustainable way. This study used varying amounts of H 2 O 2 (0, 0.5, 1.5, 3, and 5) mM/L to extract the dye from the aqueous solution. Furthermore, concentrations of 0.4, 1, 1.7, and 2.3 mM/L of Fe +2 as FeSO 4 •7H 2 O were also utilized. Batch Advanced Oxidation Process (AOP) was carried out under various working conditions, including: contact time (5-60 min), mixing speed (100-300 rpm), and UV light intensity (0-40 W). Utilizing experimental data, the AOP efficiency of Dispersed Red 17 Dye was calculated. Genetic Cascade-forward Neural Network (GCNN) was employed as a machine-learning tool to forecast the oxidation efficiency and the amount of dye that would be removed from the aqueous solution, specifically Dispersed Red 17. When compared to experimental data, the best model had an R 2 correlation value of 0.955. The findings of the importance analysis showed that the studied parameters affected the discoloration efficiency with order of: H 2 O 2 , UV, Fe +2 , mixing speed, and contact time. The obtained results demonstrated the effectiveness of GCNN as a novel approach in forecasting the AOP efficiency of Dispersed Red 17 Dye.

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Novel integrated approaches for predicting the compressibility of clay using cascade forward neural networks optimized by swarm- and evolution-based algorithms

Nguyen

et al. 2021

Acta Geotech.

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Using Hand-Dorsal Images to Reproduce Face Images by Applying Back propagation and Cascade-Forward Neural Networks

Cited by 8 publications

References 18 publications

Regenerating face images from multi-spectral palm images using multiple fusion methods

Regenerating face images from multi-spectral palm images using multiple fusion methods

Modeling of Dispersed Red 17 Dye Removal from Aqueous Solution Using Artificial Neural Network

Novel integrated approaches for predicting the compressibility of clay using cascade forward neural networks optimized by swarm- and evolution-based algorithms

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