An Improved Conditional Generative Adversarial Network for Translating Depth Image from Color Image and Accurate Hand Gesture Recognition

2021 ◽  
Author(s):  
Shurong Chai ◽  
Jiaqing Liu ◽  
Tomoko Tateyama ◽  
Yutaro Iwamoto ◽  
Yen-Wei Chen
Keyword(s):  
Gesture Recognition ◽  
Color Image ◽  
Hand Gesture Recognition ◽  
Depth Image ◽  
Hand Gesture ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals Semi-Supervised Joint Learning for Hand Gesture Recognition from a Single Color Image

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 1007
Author(s):  
Chi Xu ◽  
Yunkai Jiang ◽  
Jun Zhou ◽  
Yi Liu
Keyword(s):  
Gesture Recognition ◽  
Pose Estimation ◽  
Color Image ◽  
Recognition Performance ◽  
Recognition Task ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Estimation Task ◽  
Hand Pose Estimation ◽  
Hand Pose

Hand gesture recognition and hand pose estimation are two closely correlated tasks. In this paper, we propose a deep-learning based approach which jointly learns an intermediate level shared feature for these two tasks, so that the hand gesture recognition task can be benefited from the hand pose estimation task. In the training process, a semi-supervised training scheme is designed to solve the problem of lacking proper annotation. Our approach detects the foreground hand, recognizes the hand gesture, and estimates the corresponding 3D hand pose simultaneously. To evaluate the hand gesture recognition performance of the state-of-the-arts, we propose a challenging hand gesture recognition dataset collected in unconstrained environments. Experimental results show that, the gesture recognition accuracy of ours is significantly boosted by leveraging the knowledge learned from the hand pose estimation task.

scholarly journals Real-Time 3D Hand Gesture Recognition from Depth Image

2013 ◽  
Vol 765-767 ◽  
pp. 2826-2829 ◽  
Author(s):  
Song Lin ◽  
Rui Min Hu ◽  
Yu Lian Xiao ◽  
Li Yu Gong
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Point Cloud ◽  
Hand Gesture Recognition ◽  
Microsoft Kinect ◽  
Depth Image ◽  
Support Vector ◽  
Hand Gesture ◽  
Depth Information ◽  
Moment Invariant

In this paper, we propose a novel real-time 3D hand gesture recognition algorithm based on depth information. We segment out the hand region from depth image and convert it to a point cloud. Then, 3D moment invariant features are computed at the point cloud. Finally, support vector machine (SVM) is employed to classify the shape of hand into different categories. We collect a benchmark dataset using Microsoft Kinect for Xbox and test the propose algorithm on it. Experimental results prove the robustness of our proposed algorithm.

scholarly journals Development of Real-Time Hand Gesture Recognition for Tabletop Holographic Display Interaction Using Azure Kinect

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4566
Author(s):  
Chanhwi Lee ◽  
Jaehan Kim ◽  
Seoungbae Cho ◽  
Jinwoong Kim ◽  
Jisang Yoo ◽  
...  
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Depth Image ◽  
Hand Gesture ◽  
Leap Motion ◽  
Hand Gestures ◽  
Actual Object ◽  
Holographic Display ◽  
Continuous Rotation

The use of human gesturing to interact with devices such as computers or smartphones has presented several problems. This form of interaction relies on gesture interaction technology such as Leap Motion from Leap Motion, Inc, which enables humans to use hand gestures to interact with a computer. The technology has excellent hand detection performance, and even allows simple games to be played using gestures. Another example is the contactless use of a smartphone to take a photograph by simply folding and opening the palm. Research on interaction with other devices via hand gestures is in progress. Similarly, studies on the creation of a hologram display from objects that actually exist are also underway. We propose a hand gesture recognition system that can control the Tabletop holographic display based on an actual object. The depth image obtained using the latest Time-of-Flight based depth camera Azure Kinect is used to obtain information about the hand and hand joints by using the deep-learning model CrossInfoNet. Using this information, we developed a real time system that defines and recognizes gestures indicating left, right, up, and down basic rotation, and zoom in, zoom out, and continuous rotation to the left and right.

scholarly journals Hand gesture recognition based on free-form contours and probabilistic inference

2012 ◽  
Vol 22 (2) ◽  
pp. 437-448 ◽  
Author(s):  
Włodzimierz Kasprzak ◽  
Artur Wilkowski ◽  
Karol Czapnik
Keyword(s):  
Gesture Recognition ◽  
Color Image ◽  
Vision System ◽  
Probabilistic Inference ◽  
Hand Gesture Recognition ◽  
Free Form ◽  
Hand Gesture ◽  
Inference Methods ◽  
Viterbi Search ◽  
Hand Pose

Hand gesture recognition based on free-form contours and probabilistic inference A computer vision system is described that captures color image sequences, detects and recognizes static hand poses (i.e., "letters") and interprets pose sequences in terms of gestures (i.e., "words"). The hand object is detected with a double-active contour-based method. A tracking of the hand pose in a short sequence allows detecting "modified poses", like diacritic letters in national alphabets. The static hand pose set corresponds to hand signs of a thumb alphabet. Finally, by tracking hand poses in a longer image sequence, the pose sequence is interpreted in terms of gestures. Dynamic Bayesian models and their inference methods (particle filter and Viterbi search) are applied at this stage, allowing a bi-driven control of the entire system.

scholarly journals Real Time Hand Gesture Recognition in Depth Image using CNN

2021 ◽  
Vol 174 (16) ◽  
pp. 28-32
Author(s):  
Dardina Tasmere ◽  
Boshir Ahmed ◽  
Sanchita Rani Das
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Hand Gesture Recognition ◽  
Depth Image ◽  
Hand Gesture
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