Natural Gesture Recognition Based on Motion Detection and Skin Color

2013 ◽  
Vol 321-324 ◽  
pp. 974-979
Author(s):  
Kai Ping Feng ◽  
Ke Wan ◽  
Na Luo
Keyword(s):  
Gesture Recognition ◽  
Motion Detection ◽  
Skin Color ◽  
Object Motion ◽  
Hand Gesture ◽  
Color Analysis ◽  
Human Machine Interaction ◽  
Hand Motion ◽  
Color Features ◽  
Hand Region

With the development of the Virtual Reality technology and the next Human-Machine Interaction technology, this paper focus on the object motion detection and object skin color analysis, provide one kind of hand gesture segmentation method based on one camera. This method capture the image from the single camera to detect the moving object by the time difference method and the Gaussian module method, tracking the hand motion region real time, then to segment the hand gesture using the specified region skin color features after the hand region is extracted. Using the motion detection and the skin color features both, to do static gesture recognition by the template match method after extracting the features of the static gesture contour.This experiment make clear that the segmentation has better effect and recognition result.

scholarly journals Understanding the hand-gestures using Convolutional Neural Networks and Generative Adversial Networks

2021 ◽  
Author(s):  
Arpita Vats
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Skin Color ◽  
Recognition System ◽  
Hand Tracking ◽  
Hand Gesture ◽  
Camshift Algorithm ◽  
Hand Region

<p>In this paper, it is introduced a hand gesture recognition system to recognize the characters in the real time. The system consists of three modules: real time hand tracking, training gesture and gesture recognition using Convolutional Neural Networks. Camshift algorithm and hand blobs analysis for hand tracking are being used to obtain motion descriptors and hand region. It is fairy robust to background cluster and uses skin color for hand gesture tracking and recognition. Furthermore, the techniques have been proposed to improve the performance of the recognition and the accuracy using the approaches like selection of the training images and the adaptive threshold gesture to remove non-gesture pattern that helps to qualify an input pattern as a gesture. In the experiments, it has been tested to the vocabulary of 36 gestures including the alphabets and digits, and results effectiveness of the approach.</p>

scholarly journals Understanding the hand-gestures using Convolutional Neural Networks and Generative Adversial Networks

2021 ◽  
Author(s):  
Arpita Vats
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Gesture Recognition ◽  
Skin Color ◽  
Recognition System ◽  
Hand Tracking ◽  
Hand Gesture ◽  
Camshift Algorithm ◽  
Hand Region

<p>In this paper, it is introduced a hand gesture recognition system to recognize the characters in the real time. The system consists of three modules: real time hand tracking, training gesture and gesture recognition using Convolutional Neural Networks. Camshift algorithm and hand blobs analysis for hand tracking are being used to obtain motion descriptors and hand region. It is fairy robust to background cluster and uses skin color for hand gesture tracking and recognition. Furthermore, the techniques have been proposed to improve the performance of the recognition and the accuracy using the approaches like selection of the training images and the adaptive threshold gesture to remove non-gesture pattern that helps to qualify an input pattern as a gesture. In the experiments, it has been tested to the vocabulary of 36 gestures including the alphabets and digits, and results effectiveness of the approach.</p>

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.

scholarly journals FGFF Descriptor and Modified Hu Moment-Based Hand Gesture Recognition

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6525
Author(s):  
Beiwei Zhang ◽  
Yudong Zhang ◽  
Jinliang Liu ◽  
Bin Wang
Keyword(s):  
Gesture Recognition ◽  
Poor Performance ◽  
Hand Gesture Recognition ◽  
Hand Gesture ◽  
Discriminative Feature ◽  
Hu Moments ◽  
Gradient Orientation ◽  
Hand Region ◽  
Hu Moment ◽  
Hu Moment Invariants

Gesture recognition has been studied for decades and still remains an open problem. One important reason is that the features representing those gestures are not sufficient, which may lead to poor performance and weak robustness. Therefore, this work aims at a comprehensive and discriminative feature for hand gesture recognition. Here, a distinctive Fingertip Gradient orientation with Finger Fourier (FGFF) descriptor and modified Hu moments are suggested on the platform of a Kinect sensor. Firstly, two algorithms are designed to extract the fingertip-emphasized features, including palm center, fingertips, and their gradient orientations, followed by the finger-emphasized Fourier descriptor to construct the FGFF descriptors. Then, the modified Hu moment invariants with much lower exponents are discussed to encode contour-emphasized structure in the hand region. Finally, a weighted AdaBoost classifier is built based on finger-earth mover’s distance and SVM models to realize the hand gesture recognition. Extensive experiments on a ten-gesture dataset were carried out and compared the proposed algorithm with three benchmark methods to validate its performance. Encouraging results were obtained considering recognition accuracy and efficiency.

Effective Communication with Hand Gesture Recognition Using Voice Over and Crime Detection System for Disabled People

2020 ◽  
Vol 17 (11) ◽  
pp. 4934-4937
Author(s):  
Anitha Ponraj ◽  
Derangula Ajay Babu ◽  
Dasari Jagadish ◽  
R. Aroul Canessane ◽  
M. S. Roobini
Keyword(s):  
Gesture Recognition ◽  
Detection System ◽  
Current System ◽  
Criminal Activity ◽  
Effective Communication ◽  
Hand Gesture ◽  
Disabled People ◽  
Huge Number ◽  
Hand Motion ◽  
Crime Detection

The Hand motions are the most well-known types of correspondence and have extraordinary significance in our reality. They can help in building sheltered and agreeable UIs for a large number of uses. In the current system, we have to speak with the Deaf and moronic individuals utilizing Deaf and idiotic language just, there is no programmed device to change over that into sound arrangement. In the Proposed system, Hand motions are the most widely recognized types of correspondence and have incredible significance reality. It is used to build protected and agreeable UIs for huge number of uses. Various types of calculations have utilized on camera for hand motion acknowledgment, yet hearty implementation on motions from different subjects is as yet testing. In the Modification, We convey Hand motion acknowledgment alongside Criminal stance additionally to distinguish and anticipate any criminal activities by any client. So this application is utilized for Deaf and idiotic correspondence and voice over and well criminal activity discovery utilizing matlab.

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.

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