An Improved Gesture Segmentation Method for Gesture Recognition Based on CNN and YCbCr

Luo, Yan; Cui, Gaoxiang; Li, Deguang

doi:10.1155/2021/1783246

Cited by 11 publications

(9 citation statements)

References 27 publications

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“…This conversion stage needs to be done because it provides several advantages in the data training process on the model. The binary thresholding technique converts the background image into white and the hand object into black, often called skin color detection [18], [19]. In addition to the data thresholding process, constant illuminance control is performed in the environmental system.…”

Section: Data Pre-processingmentioning

confidence: 99%

Hand gesture-based automatic door security system using squeeze and excitation residual networks

Prihanto,

Effendy,

Nopriadi

2024

IJ-AI

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Viruses can be transmitted due to various aspects; one spreads through airborne droplets or the touch of multiple objects. This can occur in any area, including the entrance to the house or access to a room or deposit box. The spread of viruses that cause diseases like Covid-19 has caused many human casualties, and there is still the possibility of similar conditions appearing in the future. Several things need to be done to reduce the chances of spreading disease due to viruses, including developing contactless security support methods. This paper proposes a security system using hand gesture recognition using squeeze and excitation residual networks (SE-ResNet). This research offers a hand gesture recognition system for an automatic door system using SE-ResNet and the residual network (ResNet).

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Section: Data Pre-processingmentioning

confidence: 99%

Hand gesture-based automatic door security system using squeeze and excitation residual networks

Prihanto,

Effendy,

Nopriadi

2024

IJ-AI

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“…However, this method costs a lot of 3D extraction features using kinect sensors which require high equipment budget. Hand recognition system can also been developed using a neural network based on the shape fitting to resolve memory and power budgets [4] [7]. Another approach for hand gesture recognition is used from finger angle estimation based on barometric pressure sensing [8] and also ultrasonicbased hand gesture recognition system.…”

Section: Related Workmentioning

confidence: 99%

“…This work has been developed and used for hand recognition, due to its strong resistance to translation, rotation, and scaling. The most popular method for hand gesture recognition uses convolutional neural network (CNN) [1], [2], [3], [4]. This work requires analyzing a large amount of image data and analyzing the effects of data augmentation.…”

Section: Related Workmentioning

confidence: 99%

“…Along with the advancement of device control, the implementation of hand gesture recognition is increasingly being used as a basic human-computer interaction. However, considering environmental constraints, light sources, enclosed areas, and other factors, the diversity and complexity of gestures has a major impact on gesture recognition [4]. In many applications, the hand occupies only about 10% of the image, Spatial localization of the hand in such a scenario can be a challenging task [6].…”

Section: Introductionmentioning

confidence: 99%

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Hand Gesture Recognition Based on Keypoint Vector

Arwoko

Yuniarno

Purnomo

2022

2022 International Electronics Symposium (IES)

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Human-computer interaction (HCI) is usually associated with using popular input devices such as a mouse or keyboard. In other cases hand gestures can actually be useful for human-computer interaction when hand gestures are needed to make the game controls more interesting. There are three basic controls as input mouse: move, click, and drag. Hand gestures and hand shape are different for each person. This becomes a problem during automatic recognition. Recent research has proven the success of the Deep Neural Network (DNN) for representation and high accuracy in hand gesture recognition. DNN algorithms can study complex and nonlinear relationships between features by applying multiple layers. This paper proposes hand feature based on the normalized keypoint vector using DNN. The model was trained on 2250 hand datasets which were divided into 3 classes to identify the mouse movement. The network design uses multilayer with neuron sizes (13, 12, 15, 14) with 500 epochs and achieves the best accuracy of 98.5% for normalized features. The important work in this research is the use of keypoint vector from hand gestures as features to be fed to the DNN to achieve good accuracy.

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“…It allows users to cooperate with an electronic device without physical interaction; this is especially helpful during the COVID-19 pandemic. In terms of gesture numbers, existing gesture-based writing systems are limited [2][3][4][5][6][7]. Conventionally, a specific combination of a gesture and a posture symbolizes a number or character, but not all characters are often represented.…”

Section: Introductionmentioning

confidence: 99%

TARNet: An Efficient and Lightweight Trajectory-Based Air-Writing Recognition Model Using a CNN and LSTM Network

Alam

Imtiaz

Hossain

et al. 2022

Human Behavior and Emerging Technologies

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Air-writing is a growing research topic in the field of gesture-based writing systems. This research proposes a unified, lightweight, and general-purpose deep learning algorithm for a trajectory-based air-writing recognition network (TARNet). We combine a convolutional neural network (CNN) with a long short-term memory (LSTM) network. The architecture and applications of CNN and LSTM networks differ. LSTM is good for time series prediction yet time-consuming; on the other hand, CNN is superior in feature generation but comparatively faster. In this network, the CNN and LSTM serve as a feature generator and a recognizer, optimizing the time and accuracy, respectively. The TARNet utilizes 1-dimensional separable convolution in the first part to obtain local contextual features from low-level data (trajectories). The second part employs the recurrent algorithm to acquire the dependency of high-level output. Four publicly available air-writing digit (RealSense trajectory digit), character (RealSense trajectory character), smart-band, and Abas datasets were employed to verify the accuracy. Both the normalized and nonnormalized conditions were considered. The use of normalized data required longer training times but provided better accuracy. The test time was the same as those for nonnormalized data. The accuracy for RTD, RTC, smart-band, and Abas datasets were 99.63%, 98.74%, 95.62%, and 99.92%, respectively.

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An Improved Gesture Segmentation Method for Gesture Recognition Based on CNN and YCbCr

Cited by 11 publications

References 27 publications

Hand gesture-based automatic door security system using squeeze and excitation residual networks

Hand gesture-based automatic door security system using squeeze and excitation residual networks

Hand Gesture Recognition Based on Keypoint Vector

TARNet: An Efficient and Lightweight Trajectory-Based Air-Writing Recognition Model Using a CNN and LSTM Network

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