Convolutional neural networks framework for human hand gesture recognition

Mahmoud, Aseel Ghazi; Hasan, Ahmed Mudheher; Hassan, Nadia Moqbel

doi:10.11591/eei.v10i4.2926

Cited by 12 publications

(9 citation statements)

References 18 publications

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“…CNN's are usually merged in a basic network e.g. image classification [26][27][28]. Another structure of supervised learning is the RNN.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Predictive Maintenance of Electromechanical Systems Using Deep Learning Algorithms: Review

Abood¹,

Nasser²,

Al-Khazraji³

2022

ISI

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Predictive Maintenance (PM) is a major part of smart manufacturing in the fourth industrial revolution. The classical fault diagnosis approach for complex systems such as an electromechanical system is not effective. Taking the advantage of the successful implementations of PM together with Deep Learning (DL) methods replaces the conventional diagnosis methods with modern diagnosis methods. This study intends to aid experts, engineers, and technicians in different electromechanical systems in comprehending how the PM used DL methods to find the multi-fault diagnosis. In this direction, this paper presents a comprehensive review of recent works of DL techniques that are applied to PM for electromechanical systems by classifying the research according to equipment, fault, parameters, and method. To perform the review, 30 papers that are published in proceedings and journals are reviewed within a time window between the years 2016 to 2022. In the context of the electromechanical system, it is observed that motors are the most equipment selected for PM. Moreover, stator winding faults are found to be less selected than bearing for diagnosis of the unhealthy status of the motor. In terms of DL methods, the study reveals that AE, LSTM, and CNN are mostly used. In addition, poorly mixed models of DL methods are also noticed. Finally, finding the optimal design variable of the DL architecture was not widely explored.

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“…CNN's are usually merged in a basic network e.g. image classification [26][27][28]. Another structure of supervised learning is the RNN.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Predictive Maintenance of Electromechanical Systems Using Deep Learning Algorithms: Review

Abood¹,

Nasser²,

Al-Khazraji³

2022

ISI

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“…These surveys covered a large number of studies published in the research community. The deep learning-based approaches [10], [11] have been shown to outperform handcrafted feature-based approaches [5], [12]- [14] in most relevant tasks of hand such detection [15], pose estimation [5], [7], and gesture recognition [16], [17]. The convolution neuron network (CNN) architectures [18]- [20] require a very large dataset [21], [22] to train models while existing hand gesture datasets have not adapted for this demand.…”

Section: Introductionmentioning

confidence: 99%

New blender-based augmentation method with quantitative evaluation of CNNs for hand gesture recognition

Doan

Nguyen

2023

IJEECS

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<p>In this study, we extensively analyze and evaluate the performance of recent deep neural networks (DNNs) for hand gesture recognition and static gestures in particular. To this end, we captured an unconstrained hand dataset with complex appearances, shapes, scales, backgrounds, and viewpoints. We then deployed some new trending convolution neuron networks (CNNs) for gesture classification. We arrived at three major conclusions: i) DenseNet121 architecture is the best recognition rate through almost evaluated red, green, blue (RGB) and augmentation datasets. Its performance is outstanding in most original works; ii) blender-based augmentation help to significantly increase 9% of accuracy, compared to the use of a RGB cues; iii) most CNNs can achieve impressive results at 97% accuracy when the training and testing datasets come from the same lab-based or constrained environment. Their performance is drastically reduced when dealing with gestures collected in unconstrained environments. In particular, we validated the best CNN on a new unconstrained dataset. We observed a significant reduction with an accuracy of only 74.55%. This performance can be improved up to 80.59% by strategies such as blender-based and/or GAN-based data augmentations to obtain an acceptable result of 83.17%. These findings contribute crucial factors and make fruitful recommendations for the development of a robust hand-based interface in practice</p>

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“…A study of anomaly detection in surveillance video used the ROC for evaluating model performance [16], [17]. However, the confusion matrix method is the most commonly used in analyzing the classifier performance [12], [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Explicit kissing scene detection in cartoon using convolutional long short-term memory

Fadzli¹,

Hassan²,

Ibrahim³

2022

Bulletin EEI

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The main concern of this study is due to certain cartoon content consisting of explicit scenes such as kissing, sex, violence. That are somehow not suitable for kids and may contradict to some religions and cultures. There are some reasons the film industry does not expel the kissing scene in a cartoon movie. It is categorized as a romance sequence and love scene. These could be a double-edged weapon that will ruin an individual’s childhood through excessive exposure to explicit content. This paper proposes a deep learning-based classifier to detect the kissing scene in the cartoon by using Darknet-19 for frame-level feature extraction, while the feature aggregation in the temporal domain is using convolutional long short-term memory (conv-LSTM). This paper also has discussed a few steps related to evaluation and analysis regarding the performance of the models. Extensive experiments prove that the proposed system provides excellent results of 96.43% accuracy to detect the kissing scene in the cartoon. Due to high accuracy performance, the model is suitable to be a kissing scene filter feature in a digital video player that may able to decrease the excessive exposure to explicit content for kids.

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Convolutional neural networks framework for human hand gesture recognition

Cited by 12 publications

References 18 publications

Predictive Maintenance of Electromechanical Systems Using Deep Learning Algorithms: Review

Predictive Maintenance of Electromechanical Systems Using Deep Learning Algorithms: Review

New blender-based augmentation method with quantitative evaluation of CNNs for hand gesture recognition

Explicit kissing scene detection in cartoon using convolutional long short-term memory

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