Recognition of Static Hand Gesture

Sadeddine, Khadidja; Djeradi, Rachida; Chelali, Fatma Zohra; Djéradi, Amar

doi:10.1109/icmcs.2018.8525908

Cited by 11 publications

(5 citation statements)

References 11 publications

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“…On the other hand, the deep learning approach predominantly relies on the utilization of deep neural networks for representation learning and classification. Sadeddine et al (2018) [6] performed hand posture recognition in three phases: hand detection, feature extraction, and classification. In the hand detection phase, Otsu thresholding and binary operations were applied to identify the hand posture region.…”

Section: Related Workmentioning

confidence: 99%

LAVRF: Sign language recognition via Lightweight Attentive VGG16 with Random Forest

Ewe,

Lee,

Lim

et al. 2024

PLoS ONE

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Sign language recognition presents significant challenges due to the intricate nature of hand gestures and the necessity to capture fine-grained details. In response to these challenges, a novel approach is proposed—Lightweight Attentive VGG16 with Random Forest (LAVRF) model. LAVRF introduces a refined adaptation of the VGG16 model integrated with attention modules, complemented by a Random Forest classifier. By streamlining the VGG16 architecture, the Lightweight Attentive VGG16 effectively manages complexity while incorporating attention mechanisms that dynamically concentrate on pertinent regions within input images, resulting in enhanced representation learning. Leveraging the Random Forest classifier provides notable benefits, including proficient handling of high-dimensional feature representations, reduction of variance and overfitting concerns, and resilience against noisy and incomplete data. Additionally, the model performance is further optimized through hyperparameter optimization, utilizing the Optuna in conjunction with hill climbing, which efficiently explores the hyperparameter space to discover optimal configurations. The proposed LAVRF model demonstrates outstanding accuracy on three datasets, achieving remarkable results of 99.98%, 99.90%, and 100% on the American Sign Language, American Sign Language with Digits, and NUS Hand Posture datasets, respectively.

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Section: Related Workmentioning

confidence: 99%

LAVRF: Sign language recognition via Lightweight Attentive VGG16 with Random Forest

Ewe,

Lee,

Lim

et al. 2024

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Sadeddine et al (2018) [2] proposed an implementation of hand posture recognition using several descriptors on three different databases, namely American Sign Language (ASL), Arabic Sign Language (ArSL) and the NUS Hand Posture dataset. The system architecture was categorized into three phases, namely hand detection, feature extraction and classification.…”

Section: Related Workmentioning

confidence: 99%

Hand Gesture Recognition via Lightweight VGG16 and Ensemble Classifier

et al. 2022

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Gesture recognition has been studied for a while within the fields of computer vision and pattern recognition. A gesture can be defined as a meaningful physical movement of the fingers, hands, arms, or other parts of the body with the purpose to convey information for the environment interaction. For instance, hand gesture recognition (HGR) can be used to recognize sign language which is the primary means of communication by the deaf and mute. Vision-based HGR is critical in its application; however, there are challenges that will need to be overcome such as variations in the background, illuminations, hand orientation and size and similarities among gestures. The traditional machine learning approach has been widely used in vision-based HGR in recent years but the complexity of its processing has been a major challenge—especially on the handcrafted feature extraction. The effectiveness of the handcrafted feature extraction technique was not proven across various datasets in comparison to deep learning techniques. Therefore, a hybrid network architecture dubbed as Lightweight VGG16 and Random Forest (Lightweight VGG16-RF) is proposed for vision-based hand gesture recognition. The proposed model adopts feature extraction techniques via the convolutional neural network (CNN) while using the machine learning method to perform classification. Experiments were carried out on publicly available datasets such as American Sign Language (ASL), ASL Digits and NUS Hand Posture dataset. The experimental results demonstrate that the proposed model, a combination of lightweight VGG16 and random forest, outperforms other methods.

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“…However, similar gestures such as "Dal" and "Thal" were misclassified which resulted in 93.5% accuracy. Lately, the authors in [26] used two different neural networks and four visual descriptors to address the sign language recognition problem. In particular, they used the 30 letters ArSL dataset in [24] in which all images have a solid background, and the hand is the only object within the image.…”

Section: Related Workmentioning

confidence: 99%

“…ArSL recognition systems that have been reported in the literature tackled the problem using different ways. Some works [20] [22][31] [26] bypassed the segmentation stage by restricting the input images to have a uniform background resulting in easier extraction of hand shape. Other approaches opted to use external equipments to aid correct capturing of the hand gesture, such as in [14][39], a Kinect sensor that captures the intensity and the depth of the images was employed.…”

Section: Related Workmentioning

confidence: 99%

Arabic Sign Language Recognition using Faster R-CNN

Alawwad¹,

Bchir²,

Ismail³

2021

IJACSA

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Deafness does not restrict its negative effect on the person's hearing, but rather on all aspect of their daily life. Moreover, hearing people aggravated the issue through their reluctance to learn sign language. This resulted in a constant need for human translators to assist deaf person which represents a real obstacle for their social life. Therefore, automatic sign language translation emerged as an urgent need for the community. The availability and the widespread use of mobile phones equipped with digital cameras promoted the design of image-based Arabic Sign Language (ArSL) recognition systems. In this work, we introduce a new ArSL recognition system that is able to localize and recognize the alphabet of the Arabic sign language using a Faster Region-based Convolutional Neural Network (R-CNN). Specifically, faster R-CNN is designed to extract and map the image features, and learn the position of the hand in a given image. Additionally, the proposed approach alleviates both challenges; the choice of the relevant features used to encode the sign visual descriptors, and the segmentation task intended to determine the hand region. For the implementation and the assessment of the proposed Faster R-CNN based sign recognition system, we exploited VGG-16 and ResNet-18 models, and we collected a real ArSL image dataset. The proposed approach yielded 93% accuracy and confirmed the robustness of the proposed model against drastic background variations in the captured scenes.

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Recognition of Static Hand Gesture

Cited by 11 publications

References 11 publications

LAVRF: Sign language recognition via Lightweight Attentive VGG16 with Random Forest

LAVRF: Sign language recognition via Lightweight Attentive VGG16 with Random Forest

Hand Gesture Recognition via Lightweight VGG16 and Ensemble Classifier

Arabic Sign Language Recognition using Faster R-CNN

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