scholarly journals AN EFFICIENT METHOD FOR HAND GESTURE RECOGNITION USING ROBUST FEATURES VECTOR

2021 ◽  
Vol 6 (22) ◽  
pp. 25-35
Author(s):  
A F M Saifuddin Saif ◽  
Zainal Rasyid Mahayuddin
Keyword(s):  
Gesture Recognition ◽  
Efficient Method ◽  
Industrial Revolution ◽  
Hessian Matrix ◽  
Vital Role ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Natural Mode ◽  
Interest Points ◽  
Human Machine Interaction

Integration of technology for the Fourth Industrial Revolution (IR 4.0) has increased the need for efficient methods for developing dynamic human computer interfaces and virtual environments. In this context, hand gesture recognition can play a vital role to serve as a natural mode of interactive human machine interaction. Unfixed brightness, complex backgrounds, color constraints, dependency on hand shape, rotation, and scale variance are the challenging issues which have an impact on robust performance for the existing methods as per outlined in previous researches. This research presents an efficient method for hand gesture recognition by constructing a robust features vector. The proposed method is performed in two phases, where in the first phase the features vector is constructed by selecting interest points at distinctive locations using a blob detector based on Hessian matrix approximation. After detecting the area of the hand from the features vector, edge detection is applied in the isolated hand followed by edge orientation computation. After this, templates are generated using one and two dimensional mapping to compare candidate and prototype images using adaptive threshold. The proposed research performed extensive experimentation, where a recognition accuracy rate of 98.33% was achieved by it, which is higher as compared to previous research results. Experimental results reveal the effectiveness of the proposed methodology in real time.

Hand Gesture Recognition Algorithm: A Real-Time Human-Body-Based Approach

2013 ◽  
Vol 303-306 ◽  
pp. 1338-1343
Author(s):  
Xin Xiong Li ◽  
Yi Xiong ◽  
Zhi Yong Pang ◽  
Di Hu Chen
Keyword(s):  
Real Time ◽  
Gesture Recognition ◽  
Human Body ◽  
Skin Color ◽  
Average Rate ◽  
Vital Role ◽  
Recognition Algorithm ◽  
Hand Gesture Recognition ◽  
High Tech ◽  
Hand Gesture

Despite the appearance of high-tech human computer interface (HCI) devices, pattern recognition and gesture recognition with single camera are still playing vital role in research. A real-time human-body based algorithm for hand gesture recognition is proposed in this paper. The basis of our approach is a combination of moving object segmentation process and skin color detector based on human body structure to obtain the moving hands from input images, which is able to deal with the problem of complex background and random noises, and a rotate correction process for better finger detection. With ten fingers detected, more than 1000 gestures can be recognized before concerning motion paths. This paper includes experimental results of five gestures, which can be extended to other conditions. Experiments show that the algorithm can achieve a 99 percent recognition average rate and is suitable for real-time applications.

scholarly journals Hand Gesture Recognition Using an IR-UWB Radar with an Inception Module-Based Classifier

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 564 ◽  
Author(s):  
Shahzad Ahmed ◽  
Sung Ho Cho
Keyword(s):  
Gesture Recognition ◽  
Human Subjects ◽  
Three Dimensional ◽  
Hand Gesture Recognition ◽  
Ultra Wideband ◽  
Hand Gesture ◽  
Natural Mode ◽  
Hand Gestures ◽  
Daily Lives ◽  
Deep Learning Network

The emerging integration of technology in daily lives has increased the need for more convenient methods for human–computer interaction (HCI). Given that the existing HCI approaches exhibit various limitations, hand gesture recognition-based HCI may serve as a more natural mode of man–machine interaction in many situations. Inspired by an inception module-based deep-learning network (GoogLeNet), this paper presents a novel hand gesture recognition technique for impulse-radio ultra-wideband (IR-UWB) radars which demonstrates a higher gesture recognition accuracy. First, methodology to demonstrate radar signals as three-dimensional image patterns is presented and then, the inception module-based variant of GoogLeNet is used to analyze the pattern within the images for the recognition of different hand gestures. The proposed framework is exploited for eight different hand gestures with a promising classification accuracy of 95%. To verify the robustness of the proposed algorithm, multiple human subjects were involved in data acquisition.

scholarly journals Kinect-Based Vision System of Mine Rescue Robot for Low Illuminous Environment

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Wanfeng Shang ◽  
Xiangang Cao ◽  
Hongwei Ma ◽  
Hailong Zang ◽  
Pengkang Wei
Keyword(s):  
Gesture Recognition ◽  
Template Matching ◽  
Vision System ◽  
Somatosensory System ◽  
Depth Map ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Human Machine Interaction ◽  
Rescue Robot ◽  
Mine Rescue

This paper presents Kinect-based vision system of mine rescue robot working in illuminous underground environment. The somatosensory system of Kinect is used to realize the hand gesture recognition involving static hand gesture and action. AK-curvature based convex detection method is proposed to fit the hand contour with polygon. In addition, the hand action is completed by using the NiTE library with the framework of hand gesture recognition. In addition, the proposed method is compared with BP neural network and template matching. Furthermore, taking advantage of the information of the depth map, the interface of hand gesture recognition is established for human machine interaction of rescue robot. Experimental results verify the effectiveness of Kinect-based vision system as a feasible and alternative technology for HMI of mine rescue robot.

scholarly journals Facilitate sEMG-Based Human–Machine Interaction Through Channel Optimization

2019 ◽  
Vol 16 (04) ◽  
pp. 1941001 ◽  
Author(s):  
Zheng Wang ◽  
Yinfeng Fang ◽  
Gongfa Li ◽  
Honghai Liu
Keyword(s):  
Genetic Algorithm ◽  
Gesture Recognition ◽  
Degrees Of Freedom ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Human Machine Interaction ◽  
Prosthetic Hands ◽  
Channel Reduction ◽  
Machine Interaction ◽  
Channel Combination

Electromyography (EMG) has been widely accepted to interact with prosthetic hands, but still limited to using few channels for the control of few degrees of freedom. The use of more channels can improve the controllability, but it also increases system’s complexity and reduces its wearability. It is yet clear if optimizely placing the EMG channel could provide a feasible solution to this challenge. This study customized a genetic algorithm to optimize the number of channels and its position on the forearm in inter-day hand gesture recognition scenario. Our experimental results demonstrate that optimally selected 14 channels out of 16 can reach a peak inter-day hand gesture recognition accuracy at 72.3%, and optimally selecting 9 and 11 channels would reduce the performance by 3% and 10%. The cross-validation results also demonstrate that the optimally selected EMG channels from five subjects also work on the rest of the subjects, improving the accuracies by 3.09% and 4.5% in 9- and 11-channel combination, respectively. In sum, this study demonstrates the feasibility of channel reduction through genetic algorithm, and preliminary proves the significance of EMG channel optimization for human–machine interaction.

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