An Improved Hand Gesture Recognition System Based on Optimized MSVM and SIFT Feature Extraction Algorithm (original) (raw)
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Hand Gesture Recognition using fusion of SIFT and HoG with SVM as a Classifier
2017
This paper focuses on the hand gesture recognition using the various feature extraction techniques and SVM as a classifier. Her we have proposed the hybrid approach using SIFT and HoG combined as a feature extraction technique and gestures classification done using SVM linear kernel function.The accumulative multi class SVM method is employed in order to obtain a classification of the multiple gestures. In this computer age the hand gesture recognition is one of the important domain of the computer application wherein the human computer interaction is done without any contact. Various research are ongoing in order to produce the cost effective and robust system design in this field. We have also proposed our model with max 97% accuracy with 10 set of gesture. Keyword: SVM, HoG, SIFT, Hand Gesture recognition, Gesture, HC
New Method for Optimization of Static Hand Gesture Recognition System with Use of SVM
IEEE
The hand gesture recognition and pattern recognition are the growing fields of research. Gestures are the motion of the body or physical action form by the user in order to convey some meaningful information. In this paper, we propose a robust and efficient method for real-time hand gesture recognition system. In the suggested method, first, the hand gesture is extracted from the main image by edge detection and morphological operation and then is sent to feature extraction stage. In feature extraction stage modified contour chain code feature set is extracted. Finally, at classification stage, we employ multiclass support vector machine (SVM) as a classifier. In the result part, the proposed approach is applied to American Sign Language (ASL) database and the accuracy rate obtained 99.40%. Further, we obtained 99.80% accuracy using five-fold cross validation technique on ASL dataset.
2016
Hand Gesture Recognition System (HGRS) has been proved to be a powerful communication tool for deaf and dumb users, irrespective of geographical differences. HGRS is fragmented in six consequent phases applied on captured image namely; Hand Detection, Hand Tracking, Region Extraction, Feature Extraction, Feature Matching, and Pattern Recognition. We have studied various techniques in HGRS for sign languages and now present the analysis of performance of existing techniques in HGRS. Our study is presented on the basis of fragmentation used in HGRS and includes the strength and the scope of improvements for each technique. These observations will be highly useful to the researchers putting efforts in the domain of recognition of sign languages for improving the recognition rate particularly. Keywords— CAMSHIFT, GMM, Histogram, 3D Model-based detection, Particle filtering, BLOB, Kalman filter, Template matching, SVM, HMM, ArSL, ASL, DSL.
the work presented in this paper is to develop a system for automatic Recognition of static gestures of alphabets in American Sign Language. In doing so three feature extraction methods and neural network is used to recognize signs. The system recognizes images of bare hands, which allows the user to interact with the system in a natural way. An image is processed and converted to a feature vector that will be compared with the feature vectors of a training set of signs. Further work is to investigate the application of the Scale-Invariant Feature Transform (SIFT) to the problem of hand gesture recognition by using MATLAB.The algorithm uses modified SIFT approach to match key-points between the query image and the original database of Bare Hand images taken. The extracted features are highly distinctive as they are shift, scale and rotation invariant. They are also partially invariant to illumination and affine transformations. The system is implemented and tested using data sets of number of samples of hand images for each signs. Three feature extraction methods are tested and best one is suggested with results obtained from ANN. The system is able to recognize selected ASL signs with the accuracy of 92.33% using edge detection and 98.99 using sift algorithm.
International Journal on Recent and Innovation Trends in Computing and Communication
The field of hand gesture recognition has recently reached new heights thanks to its widespread use in domains like remote sensing, robotic control, and smart home appliances, among others. Despite this, identifying gestures is difficult because of the intransigent features of the human hand, which make the codes used to decode them illegible and impossible to compare. Differentiating regional patterns is the job of pattern recognition. Pattern recognition is at the heart of sign language. People who are deaf or mute may understand the spoken language of the rest of the world by learning sign language. Any part of the body may be used to create signs in sign language. The suggested system employs a gesture recognition system trained on Indian sign language. The methods of preprocessing, hand segmentation, feature extraction, gesture identification, and classification of hand gestures are discussed in this work as they pertain to hand gesture sign language. A hybrid approach is used ...
This Exploratory paper's second part reveals the detail technological aspects of Hand Gesture Recognition (HGR) System. It further explored HGR basic building blocks, its application areas and challenges it faces. The paper also provides literature review on latest upcoming techniques like -Point Grab, 3D Mouse and Sixth-Sense etc. The paper concluded with focus on major Application fields. Volume: 1 Issue: 8 632 -637 ________________________________________________________________________________ 636 IJRITCC | AUG 2013, Available @ http://www.ijritcc.org ________________________________________________________________________________ laptop, smartphone, tablet, or a smart-television into a gesturecontrolled device as shown in fig 9.
IJERT-Hand Gesture Recognition using SIFT
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/hand-gesture-recognition-using-sift https://www.ijert.org/research/hand-gesture-recognition-using-sift-IJERTV2IS1401.pdf Gesture refers to expressive movement of human body parts having a particular message to be communicated to a receiver. Gesture recognition pertains to understanding meaning of human body part movement,which involves the movement of hand, face, head, arms or body .Today's world is witnessing enormous improvements in processing speeds and visualization displays, but still input devices for the most part have lagged behind, presenting a bottleneck in applications. Gesture recognition is very important in designing an efficient HCI. This paper presents a method for recognizing hand gestures by extracting distinctive invariant features fromimages that can be used to perform efficient matching between different views ofa hand gesture. The features are invariant to image scale and rotation, and provide robust matching across a considerable range of affine distortion,change in 3D viewpoint, addition of noise, and change in illumination [1].
Analysis and Implementation of Hand Gesture Recognition System
Abstract: Hand gestures recognition provides a natural way to interact and communicate with machines of different kinds. The process is known and referred to as static hand gesture recognition in which images of a hand gesture are stored in the database and analyzed in order to determine the meaning of the hand gesture. The implementation has been done using Scale Invariant Feature Transform (SIFT), Principal Component Analysis(PCA) which present an interface used to recognize hand gestures from the American Sign Language. Keywords: Distance ratio, Threshold, convolution, Gaussian Filters, Eigen Vector. Title: Analysis and Implementation of Hand Gesture Recognition System Author: Mrs. Poonam Verma, Utkarsh Mor, Puneet Singh, Arpan Khandelwal International Journal of Computer Science and Information Technology Research ISSN 2348-1196 (print), ISSN 2348-120X (online) Research Publish Journals
IEEE Transactions on Instrumentation and Measurement, 2011
This paper presents a novel and real-time system for interaction with an application or video game via hand gestures. Our system includes detecting and tracking bare hand in cluttered background using skin detection and hand posture contour comparison algorithm after face subtraction, recognizing hand gestures via bag-of-features and multiclass support vector machine (SVM) and building a grammar that generates gesture commands to control an application. In the training stage, after extracting the keypoints for every training image using the scale invariance feature transform (SIFT), a vector quantization technique will map keypoints from every training image into a unified dimensional histogram vector (bag-of-words) after K-means clustering. This histogram is treated as an input vector for a multiclass SVM to build the training classifier. In the testing stage, for every frame captured from a webcam, the hand is detected using our algorithm, then, the keypoints are extracted for every small image that contains the detected hand gesture only and fed into the cluster model to map them into a bag-of-words vector, which is finally fed into the multiclass SVM training classifier to recognize the hand gesture.