Hand Gesture Recognition with Generalized Hough Transform and DC-CNN Using Realsense

Liao, Bo; Li, Jing; Ju, Zhaojie; Ouyang, Gaoxiang

doi:10.1109/icist.2018.8426125

Cited by 32 publications

(11 citation statements)

References 29 publications

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“…The purpose of the gesture capture sensor is to digitize the gesture. Liao B. et al [14] proposed a static recognition system based on the deep learning model. A double-channel CNN to fuse the color information and depth information data were captured by Intel RealSense300 depth camera.…”

Section: Capture Images With Hand Gesturesmentioning

confidence: 99%

Deep learning based static hand gesture recognition

Satybaldina

Kalymova

2021

IJEECS

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Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human–computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For pre-processing and image segmentation algorithms computer vision methods from the OpenCV and Intel Real Sense libraries are used. The subsystem for features extracting and gestures classification is based on the modified VGG-16 by using the TensorFlow&Keras deep learning framework. Performance of the static gestures recognition system is evaluated using maching learning metrics. Experimental results show that the proposed model, trained on a database of 2000 images, provides high recognition accuracy both at the training and testing stages.

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Section: Capture Images With Hand Gesturesmentioning

confidence: 99%

Deep learning based static hand gesture recognition

Satybaldina

Kalymova

2021

IJEECS

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“…Hand gestures also play an important role in human interactions. In 2018, B Liao et al [6] proposed a hand gesture recognition method and used the RGB-D dataset collected by Intel RealSense Front-Facing Camera SR300. The amount of information transmitted through facial expressions accounts for 55%.…”

Section: A the Facial Expression Recognition Taskmentioning

confidence: 99%

A Cascaded Feature Pyramid Network With Non-Backward Propagation for Facial Expression Recognition

et al. 2021

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In this work we propose a novel cascaded feature pyramid network with non-backward propagation (CFPN-NBP) for facial expression recognition (FER) that addresses the problems inherent in traditional backward propagation (BP) algorithms in the training process by using the Hilbert-Schmidt independence criterion (HSIC) bottleneck. The proposed algorithm is developed at two different levels. At the first level, a novel training method HSIC bottleneck is considered as an alternative to traditional BP optimization, where the correlation between the output of the hidden layers and the input, and the correlation between the output of the hidden layers and its label are calculated to reduce redundant information; hence, the least information is used to predict the results. At the second level, a novel architecture is designed in the feature extraction process. The convolutional layers with the same resolutions are densely connected and introduced into the attention mechanism, so that the model can focus on more important information. The convolutional layers with different resolutions are combined by three cascaded pyramid networks; in this way, the shallow features and the deep features can be further fused, and; therefore, the semantic information and the content information can both be reserved. To further reduce the number of parameters, the operation of separable convolution instead of traditional convolution is utilized. Experiments on the challenging FER2013 dataset show that the proposed CFPN-NBP algorithm improves the accuracy of the FER task and outperforms the related state-of-the-art methods.

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“…Despite the fact that Intel RealSense devices have only appeared on the market in recent years, they have been adopted in several fields. Among the many fields and applications, we can find posture and gesture interaction systems and human interaction design [20,21,22,23,24], interactive artificial intelligence (AI) toys for children [25], security and robotics [26,27,28], and also medical and human care [29,30,31].…”

Section: Introductionmentioning

confidence: 99%

Metrological and Critical Characterization of the Intel D415 Stereo Depth Camera

Carfagni

Furferi

Governi

et al. 2019

Sensors

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Low-cost RGB-D cameras are increasingly being used in several research fields, including human–machine interaction, safety, robotics, biomedical engineering and even reverse engineering applications. Among the plethora of commercial devices, the Intel RealSense cameras have proven to be among the most suitable devices, providing a good compromise between cost, ease of use, compactness and precision. Released on the market in January 2018, the new Intel model RealSense D415 has a wide acquisition range (i.e., ~160–10,000 mm) and a narrow field of view to capture objects in rapid motion. Given the unexplored potential of this new device, especially when used as a 3D scanner, the present work aims to characterize and to provide metrological considerations for the RealSense D415. In particular, tests are carried out to assess the device performance in the near range (i.e., 100–1000 mm). Characterization is performed by integrating the guidelines of the existing standard (i.e., the German VDI/VDE 2634 Part 2) with a number of literature-based strategies. Performance analysis is finally compared against the latest close-range sensors, thus providing a useful guidance for researchers and practitioners aiming to use RGB-D cameras in reverse engineering applications.

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Hand Gesture Recognition with Generalized Hough Transform and DC-CNN Using Realsense

Cited by 32 publications

References 29 publications

Deep learning based static hand gesture recognition

Deep learning based static hand gesture recognition

A Cascaded Feature Pyramid Network With Non-Backward Propagation for Facial Expression Recognition

Metrological and Critical Characterization of the Intel D415 Stereo Depth Camera

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