scholarly journals Real-Time Hand Gesture Recognition Using Finger Segmentation

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Zhi-hua Chen ◽  
Jung-Tae Kim ◽  
Jianning Liang ◽  
Jing Zhang ◽  
Yu-Bo Yuan
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Background Subtraction ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Subtraction Method ◽  
Hand Gestures ◽  
Data Set ◽  
Hand Region ◽  
State Of Art

Hand gesture recognition is very significant for human-computer interaction. In this work, we present a novel real-time method for hand gesture recognition. In our framework, the hand region is extracted from the background with the background subtraction method. Then, the palm and fingers are segmented so as to detect and recognize the fingers. Finally, a rule classifier is applied to predict the labels of hand gestures. The experiments on the data set of 1300 images show that our method performs well and is highly efficient. Moreover, our method shows better performance than a state-of-art method on another data set of hand gestures.

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.

Real-Time Hand Gesture Recognition

2021 ◽  
pp. 618-624
Author(s):  
Pranjali Manmode ◽  
Rupali Saha ◽  
Manisha N. Amnerkar
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Skin Color ◽  
Robot Control ◽  
Rapid Development ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Human Hand ◽  
Hand Gestures ◽  
Gesture Tracking

With the rapid development of computer vision, the demand for interaction between humans and machines is becoming more and more extensive. Since hand gestures can express enriched information, hand gesture recognition is widely used in robot control, intelligent furniture, and other aspects. The paper realizes the segmentation of hand gestures by establishing the skin color model and AdaBoost classifier based on haar according to the particularity of skin color for hand gestures and the denaturation of hand gestures with one frame of video being cut for analysis. In this regard, the human hand is segmented from a complicated background. The camshaft algorithm also realizes real-time hand gesture tracking. Then, the area of hand gestures detected in real-time is recognized by a convolutional neural network to discover the recognition of 10 common digits. Experiments show 98.3% accuracy.

scholarly journals Robust hand gesture recognition using multiple shape-oriented visual cues

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Samy Bakheet ◽  
Ayoub Al-Hamadi
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Pose Estimation ◽  
Depth Map ◽  
Hand Gesture Recognition ◽  
Support Vector ◽  
Hand Gesture ◽  
Hand Pose Estimation ◽  
Time Operation ◽  
Hand Pose

AbstractRobust vision-based hand pose estimation is highly sought but still remains a challenging task, due to its inherent difficulty partially caused by self-occlusion among hand fingers. In this paper, an innovative framework for real-time static hand gesture recognition is introduced, based on an optimized shape representation build from multiple shape cues. The framework incorporates a specific module for hand pose estimation based on depth map data, where the hand silhouette is first extracted from the extremely detailed and accurate depth map captured by a time-of-flight (ToF) depth sensor. A hybrid multi-modal descriptor that integrates multiple affine-invariant boundary-based and region-based features is created from the hand silhouette to obtain a reliable and representative description of individual gestures. Finally, an ensemble of one-vs.-all support vector machines (SVMs) is independently trained on each of these learned feature representations to perform gesture classification. When evaluated on a publicly available dataset incorporating a relatively large and diverse collection of egocentric hand gestures, the approach yields encouraging results that agree very favorably with those reported in the literature, while maintaining real-time operation.

scholarly journals Electromyogram-based hand gesture recognition robust to various arm postures

2018 ◽  
Vol 14 (7) ◽  
pp. 155014771879075 ◽  
Author(s):  
Kiwon Rhee ◽  
Hyun-Chool Shin
Keyword(s):  
Gesture Recognition ◽  
Recognition Accuracy ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Hand Gestures ◽  
Practical Applications ◽  
Electrode Positioning ◽  
Recognition Technique

In the recognition of electromyogram-based hand gestures, the recognition accuracy may be degraded during the actual stage of practical applications for various reasons such as electrode positioning bias and different subjects. Besides these, the change in electromyogram signals due to different arm postures even for identical hand gestures is also an important issue. We propose an electromyogram-based hand gesture recognition technique robust to diverse arm postures. The proposed method uses both the signals of the accelerometer and electromyogram simultaneously to recognize correct hand gestures even for various arm postures. For the recognition of hand gestures, the electromyogram signals are statistically modeled considering the arm postures. In the experiments, we compared the cases that took into account the arm postures with the cases that disregarded the arm postures for the recognition of hand gestures. In the cases in which varied arm postures were disregarded, the recognition accuracy for correct hand gestures was 54.1%, whereas the cases using the method proposed in this study showed an 85.7% average recognition accuracy for hand gestures, an improvement of more than 31.6%. In this study, accelerometer and electromyogram signals were used simultaneously, which compensated the effect of different arm postures on the electromyogram signals and therefore improved the recognition accuracy of hand gestures.

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