Deep Gesture: Static Hand Gesture Recognition Using CNN

Mohanty, Amiya Ranjan; Rambhatla, Sai Saketh; Sahay, Rajiv R.

doi:10.1007/978-981-10-2107-7_41

Cited by 53 publications

(32 citation statements)

References 15 publications

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“…There are some approaches, such as RetinaNet based [21] and ResNet50+highlight feature fusion (HFF)+Auxiliary [22], that use ResNet50 [23] as the backbone combined with other networks for detection and recognition. In [24] and [25], basic convolutional pooling layers were used to construct new detection and recognition models to improve the gesture recognition accuracy via endto-end training on datasets. In [26], a first-person perspective dataset and a CNN-based method, which can distinguish between one's own hands and the hands of others, were proposed.…”

Section: Volume XX 2017mentioning

confidence: 99%

“…Currently, deep learning-based gesture recognition methods are widely used. In [24] and [25], CNN-based methods were proposed, and the basic CNN architecture was used to construct deep learning networks for gesture recognition, which can achieve good recognition accuracy. Wan et al [44] proposed a GAN-based model for the augmentation of hand datasets to improve the gesture recognition accuracy.…”

Section: B Gesture Recognitionmentioning

confidence: 99%

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SqueezeNet and Fusion Network-Based Accurate Fast Fully Convolutional Network for Hand Detection and Gesture Recognition

et al. 2021

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Accurate fast hand detection and gesture recognition for hand understanding are still challenging tasks that are influenced by the diversity of hands and the complexity of the scene in color images. To address the above problem, we propose a novel SqueezeNet and fusion network-based fully convolutional network (SF-FCNet) to accurately and quickly perform hand detection and gesture recognition in color images. First, we introduce the first 17-layer structure in the lightweight SqueezeNet as the hand feature extraction network to accelerate the detection and recognition speed by greatly compressing the network parameters. Second, a precise hand prediction fusion network is designed by adding a residual structure to the deconvolutional network to integrate high-and low-level features of hands, and hand detection and gesture recognition are performed on a single convolutional layer at multiple scales to improve the precision and reduce the computational costs. The verification results on the Oxford hand dataset show that SF-FCNet can reach a precision of 84.1% and a speed of 32 FPS. The experimental results show that SF-FCNet can substantially enhance the precision and speed of hand detection and gesture recognition on three benchmark datasets and has a strong generalization ability on a homemade test set.

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Section: Volume XX 2017mentioning

confidence: 99%

Section: B Gesture Recognitionmentioning

confidence: 99%

SqueezeNet and Fusion Network-Based Accurate Fast Fully Convolutional Network for Hand Detection and Gesture Recognition

et al. 2021

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“…However, research works using this approach have encountered many challenges that degrade the performance of existing systems such as lighting inconsistency, motion blur, background clutter, and hands occlusion. Moreover, studies using the vision-based approach can be classified into two categories: conventional techniques (e.g., [2]- [9] and [26]- [33]) and deep learning-based techniques (e.g., [10], [11], and [34]- [41]).…”

Section: Related Workmentioning

confidence: 99%

“…Another recent approach proposed by Okan et al involved the fusion of optical flow and RGB frames to adapt the pretrained inception model for hand gesture recognition [37]. Another CNN-based architecture was proposed in [10] for static hand gesture recognition. The input to this architecture was a small image with a size of 32×32 that contains only the hand region.…”

Section: Related Workmentioning

confidence: 99%

“…Most of the existing recognition systems only consider the local configuration of the hand. These systems either receive a segmented hand region as input or perform a hand segmentation preprocessing step using skin color models or colored gloves [2,3,4,5,6,7,8,9,10]. However, such systems perform well only for gestures involving simple alphabets and numbers, which slightly rely on the global configuration, but not for real sign language gestures.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning-Based Approach for Sign Language Gesture Recognition With Efficient Hand Gesture Representation

et al. 2020

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Hand gesture recognition is an attractive research field with a wide range of applications, including video games and telesurgery techniques. Another important application of hand gesture recognition is the translation of sign language, which is a complicated structured form of hand gestures. In sign language, the fingers' configuration, the hand's orientation, and the hand's relative position to the body are the primitives of structured expressions. The importance of hand gesture recognition has increased due to the prevalence of touchless applications and the rapid growth of the hearing-impaired population. However, developing an efficient recognition system needs to overcome the challenges of hand segmentation, local hand shape representation, global body configuration representation, and gesture sequence modeling. In this paper, a novel system is proposed for dynamic hand gesture recognition using multiple deep learning architectures for hand segmentation, local and global feature representations, and sequence feature globalization and recognition. The proposed system is evaluated on a very challenging dataset, which consists of 40 dynamic hand gestures performed by 40 subjects in an uncontrolled environment. The results show that the proposed system outperforms stateof-the-art approaches, demonstrating its effectiveness.

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Design and Verification of a Security Policy to Defense DDoS Attack for Cloud Robot

Liu

Tang

Cheng

et al. 2018

Cloud Computing and Security

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Deep Gesture: Static Hand Gesture Recognition Using CNN

Cited by 53 publications

References 15 publications

SqueezeNet and Fusion Network-Based Accurate Fast Fully Convolutional Network for Hand Detection and Gesture Recognition

SqueezeNet and Fusion Network-Based Accurate Fast Fully Convolutional Network for Hand Detection and Gesture Recognition

Deep Learning-Based Approach for Sign Language Gesture Recognition With Efficient Hand Gesture Representation

Design and Verification of a Security Policy to Defense DDoS Attack for Cloud Robot

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