Hand Gesture Recognition Based on Fingertip Detection

Guoqing-Meng,; Wang, Mei

doi:10.1109/gcis.2013.23

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…First, fingertips are detected using discrete curve evolution and then the gesture is recognized by partitioning the evolved curves detected from fingertips. Similarly, Meng et al [39] approximates the contours and convexity defect to find the coordinate positions of fingertips and then the gesture is recognized by using features such as the number of fingers, the Hu moments of a region bounded by the contour, and the compactness and the convexity of detected contour. Lee et al [40] estimates the scale-invariant angle between the fingers to determine the different number of visible fingertips.…”

Section: Gesture Recognition and Fingertip Detectionmentioning

confidence: 99%

“…However, the image processingbased approaches have the dependency on background, hand shape and color thus tend to fail in complex and diverse scenarios. Moreover, the approaches that use the convex hull technique for gesture recognition and fingertip detection [39,34,37] have their instinctive disadvantage. For instance, although they can recognize the gesture and detect fingertips, they cannot classify fingers and thus cannot apprise which fingertips have been detected.…”

Section: Scope Of Analysismentioning

confidence: 99%

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Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection

Alam,

Islam,

Rahman

2021

Preprint

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In human-computer interaction or sign language interpretation, recognizing hand gestures and detecting fingertips become ubiquitous in computer vision research. In this paper, a unified approach of convolutional neural network for both hand gesture recognition and fingertip detection is introduced. The proposed algorithm uses a single network to predict the probabilities of finger class and positions of fingertips in one forward propagation of the network. Instead of directly regressing the positions of fingertips from the fully connected layer, the ensemble of the position of fingertips is regressed from the fully convolutional network. Subsequently, the ensemble average is taken to regress the final position of fingertips. Since the whole pipeline uses a single network, it is significantly fast in computation. The proposed method results in remarkably less pixel error as compared to that in the direct regression approach and it outperforms the existing fingertip detection approaches including the Heatmap-based framework.

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Section: Gesture Recognition and Fingertip Detectionmentioning

confidence: 99%

Section: Scope Of Analysismentioning

confidence: 99%

Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection

Alam,

Islam,

Rahman

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In another work, Hu moment features used by Meng et al [23] proposed an algorithm for detecting the fingertip structure. First, the features which are the areas including skin region and the image, were made to differentiate the background in space of saturation, value of brightness, and hue from the skin region.…”

Section: Hand Gesture Studiesmentioning

confidence: 99%

Hand Gesture Controlled Drones: An Open Source Library

Natarajan

Mete

2018

2018 1st International Conference on Data Intelligence and Security (ICDIS)

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Drones are conventionally controlled using joysticks, remote controllers, mobile applications, and embedded computers. A few significant issues with these approaches are that drone control is limited by the range of electromagnetic radiation and susceptible to interference noise. In this study we propose the use of hand gestures as a method to control drones. We investigate the use of computer vision methods to develop an intuitive way of agent-less communication between a drone and its operator. Computer vision-based methods rely on the ability of a drones camera to capture surrounding images and use pattern recognition to translate images to meaningful and/or actionable information. The proposed framework involves a few key parts toward an ultimate action to be taken. They are: image segregation from the video streams of front camera, creating a robust and reliable image recognition based on segregated images, and finally conversion of classified gestures into actionable drone movement, such as takeoff, landing, hovering and so forth. A set of five gestures are studied in this work. Haar feature-based AdaBoost classifier [1] is employed for gesture recognition. We also envisage safety of the operator and drone's action calculating the distance based on computer vision for this task. A series of experiments are conducted to measure gesture recognition accuracies considering the major scene variabilities, illumination, background, and distance. Classification accuracies show that well-lit, clear background, and within 3 ft gestures are recognized correctly over 90%. Limitations of current framework and feasible solutions for better gesture recognition are discussed, too. The software library we developed,and hand gesture datasets are open-sourced at project website.

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Computer vision-based hand gesture recognition for human-robot interaction: a review

Cui

et al. 2023

Complex Intell. Syst.

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As robots have become more pervasive in our daily life, natural human-robot interaction (HRI) has had a positive impact on the development of robotics. Thus, there has been growing interest in the development of vision-based hand gesture recognition for HRI to bridge human-robot barriers. The aim is for interaction with robots to be as natural as that between individuals. Accordingly, incorporating hand gestures in HRI is a significant research area. Hand gestures can provide natural, intuitive, and creative methods for communicating with robots. This paper provides an analysis of hand gesture recognition using both monocular cameras and RGB-D cameras for this purpose. Specifically, the main process of visual gesture recognition includes data acquisition, hand gesture detection and segmentation, feature extraction and gesture classification, which are discussed in this paper. Experimental evaluations are also reviewed. Furthermore, algorithms of hand gesture recognition for human-robot interaction are examined in this study. In addition, the advances required for improvement in the present hand gesture recognition systems, which can be applied for effective and efficient human-robot interaction, are discussed.

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Hand Gesture Recognition Based on Fingertip Detection

Cited by 6 publications

References 8 publications

Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection

Unified Learning Approach for Egocentric Hand Gesture Recognition and Fingertip Detection

Hand Gesture Controlled Drones: An Open Source Library

Computer vision-based hand gesture recognition for human-robot interaction: a review

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