Recognition of static hand gesture with using ANN and SVM

Hand gesture recognition (HGR) is an important research topic because some situations require silent communication with sign languages. Computational HGR systems assist silent communication, and help people learn a sign language. In this article, a novel method for contact-less HGR using Microsoft Kinect for Xbox is described, and a real-time HGR system is implemented. The system is able to detect the presence of gestures, to identify fingers, and to recognize the meanings of nine gestures in a pre-defined Popular Gesture scenario. The accuracy of the HGR system is from 84% to 99% with single-hand gestures, and from 90% to 100% if both hands perform the same gesture at the same time. Because the depth sensor of Kinect is an infrared camera, the lighting conditions, signers' skin colors and clothing, and background have little impact on the performance of this system. The accuracy and the robustness make this system a versatile component that can be integrated in a variety of applications in daily life.

Any human–computer interaction application needs to be able to recognize gestures. Hand gesture detection systems that recognize gestures in real time can improve human–computer interaction by making it more intuitive and natural. Colour gloves and skin colour detection are two prominent hand segmentation and detection technologies, but each has its own set of benefits and drawbacks. For physically challenged people, gesture identification is a crucial technique of sharing information. Support vector machine algorithm with principal component analysis, hidden Markov model, superposed network with multiple restricted Boltzmann machines, growing neural gas algorithm, convolutional neural network, double channel convolutional neural network, artificial neural network, and linear support vector machine with gesture dataset matches the interaction of gestures for various postures in real time. Although this method can recognize a huge number of gestures, it does have certain downsides, such as missing movements due to the accuracy of the categorization algorithms. Furthermore, matching the vast dataset takes a longer time. The main contribution of this work lies in a conceptual framework based on the findings of a systematic literature review that provides fruitful implications based on recent research findings and insights that can be used to direct and initiate future research initiatives in the field of hand gesture recognition and artificial intelligence. As a result, a novel study based on a hybrid recurrent neural network (RNN) with chaos game optimization may reduce classification mistakes, increase stability, maximize resilience, and efficiently use the hand gestures recognition system.

The goal of static hand gesture recognition is to classify the given hand gesture data represented by some features into some prede ned nite number of gesture classes. The main objective of this e ort is to explore the utility of two feature extraction methods, namely, hand contour and complex moments to solve the hand gesture recognition problem by identifying the primary advantages and disadvantages of each method. Arti cial neural network is built for the purpose of classi cation by using the back-propagation learning algorithm. The proposed system presents a recognition algorithm to recognize a set of six speci c static hand gestures, namely: Open, Close, Cut, Paste, Maximize, and Minimize. The hand gesture image is passed through three stages, namely, pre-processing, feature extraction, and classi cation. In the pre-processing stage some operations are applied to extract the hand gesture from its background and prepare the hand gesture image for the feature extraction stage. In the rst method, the hand contour is used as a feature which treats scaling and translation problems (in some cases). The complex moments algorithm is, however,

This article presents a robotic visual system that allows effective recognition of multiple-angle hand gestures in finger guessing games. We shot images of hand gestures at various angles to train support vector machine (SVM) for the construction of a multiple-angle hand gesture recognition system. Our experimental results show that the hand gesture recognition system presented by this article can effectively recognize hand gestures, at over 95%, of different angles and sizes, with different ornaments, and of different skin colors, while recognizing right or left hand gestures, at up to 90% recognition rate.

Hand gesture is one of the method used in sign language for non-verbal communication. It is most commonly used by deaf & dumb people who have hearing or speech problems to communicate among themselves or with normal people. Various sign language systems has been developed by many makers around the world but they are neither flexible nor cost-effective for the end users. Hence in this paper introduced software which presents a system prototype that is able to automatically recognize sign language to help deaf and dumb people to communicate more effectively with each other or normal people. Pattern recognition and Gesture recognition are the developing fields of research. Being a significant part in nonverbal communication hand gestures are playing key role in our daily life. Hand Gesture recognition system provides us an innovative, natural, user friendly way of communication with the computer which is more familiar to the human beings. By considering in mind the similarities of human hand shape with four fingers and one thumb, the software aims to present a real time system for recognition of hand gesture on basis of detection of some shape based features like orientation, Centre of mass centroid, fingers status, thumb in positions of raised or folded fingers of hand

Hand gesture recognition (HGR) systems using electromyography (EMG) bracelet-type sensors are currently largely used over other HGR technologies. However, bracelets are susceptible to electrode rotation, causing a decrease in HGR performance. In this work, HGR systems with an algorithm for orientation correction are proposed. The proposed orientation correction method is based on the computation of the maximum energy channel using a synchronization gesture. Then, the channels of the EMG are rearranged in a new sequence which starts with the maximum energy channel. This new sequence of channels is used for both training and testing. After the EMG channels are rearranged, this signal passes through the following stages: pre-processing, feature extraction, classification, and post-processing. We implemented user-specific and user-general HGR models based on a common architecture which is robust to rotations of the EMG bracelet. Four experiments were performed, taking into account two different metrics which are the classification and recognition accuracy for both models implemented in this work, where each model was evaluated with and without rotation of the bracelet. The classification accuracy measures how well a model predicted which gesture is contained somewhere in a given EMG, whereas recognition accuracy measures how well a model predicted when it occurred, how long it lasted, and which gesture is contained in a given EMG. The results of the experiments (without and with orientation correction) executed show an increase in performance from 44.5% to 81.2% for classification and from 43.3% to 81.3% for recognition in user-general models, while in user-specific models, the results show an increase in performance from 39.8% to 94.9% for classification and from 38.8% to 94.2% for recognition. The results obtained in this work evidence that the proposed method for orientation correction makes the performance of an HGR robust to rotations of the EMG bracelet.

Communication for mute people has always been very difficult. Mute people use hand gestures, also known as sign language to communicate with normal people. Hands gestures have their assigned meanings which may differ from person to person and hence cannot be understood by normal people. To overcome this difficulty many vision-based hand gesture recognition systems and data glove based hand gesture recognition systems have been proposed. This paper illustrates about two different techniques of vision-based hand gesture recognition and one data glove based technique. The vision — based techniques are static hand gesture recognition technique and real-time hand gesture recognition technique. In both the techniques, MATLAB software is used for processing input images and no dataset is used for decision making which makes this system more accurate than the existing system designs. In data glove based technique, the glove consists of five flex sensors. The change in resistance of flex sensor is used to recognize the hand gesture. All the three techniques are performed on 10 subjects and compared to find the most accurate technique. It was found that both the vision based techniques showed 100% accuracy in bright lighting condition with a white background while the data glove based technique showed an accuracy of 86%. This which clearly shows that mentioned vision based is more stable and reliable compared to the data glove based technique.

In today’s ever-evolving healthcare landscape, our objective is to bring technology closer to human experience. In this paper, a vision-based hand gesture recognition (HGR) system is introduced as a human computer interface (HCI) that transforms natural movements into intuitive medium for device and application manipulation. This innovative approach is designed to empower the elderly and enhance patient monitoring by offering a seamless, nonintrusive interface. By bridging compassion with cutting-edge technology, this approach aims to redefine care and create a more connected, responsive healthcare environment. The study begins by reviewing both traditional and contemporary HGR methodologies, with a focus on vision-based systems, which have shown greater potential for practical applications compared to conventional sensor-based systems. Traditional approaches, such as contour analysis, colour segmentation, and template matching, have demonstrated limitations. In contrast, deep learning approaches have gained mass adaptation in vision-based systems for their ability to learn hierarchical features and better handling complex, dynamic scenarios. However, existing models struggle in real-time performance due to computational inefficiencies and environmental variations. This study introduces a deep learning-based detection framework which enables the practical application of HGR. For example, it supports the development of gesture-based virtual assistants for interacting with digital devices and allows elderly residents in care homes to give instructions to remote caregivers. The framework consists of two deep learning models, one for region of interest (ROI) detection and another for classification. The recognition process begins with capturing RGB images, chosen for versatility, affordability, and compatibility, which then passes through YOLOv8 (You Only Look Once version 8) for detection. The YOLOv8 model is trained on a subset, only 4.5%, of the HaGRID dataset, comprises over 550,000 RGB images, to accurately locate hand regions. Once detection is complete, several image processing techniques are applied to overcome typical HGR constraints. These include ROI extraction and gray scaling for faster computation, resizing for model optimization and distance viability, HSV conversion for lighting independence, and histogram equalization for enhanced feature extraction. The processed image is then fed into a convolutional neural network (CNN) for gesture classification. The network is designed with several convolutional layers, followed by dense layers with ReLU activation to introduce non-linearity, and SoftMax activation for classification. To improve the model's robustness, data augmentation techniques such as random flipping and rotation are applied. The HGR system demonstrates a staggering accuracy of 97% for training & validation. The proposed framework effectively addresses the limitations of conventional HGR systems—many of which have been developed and tested in controlled environments—demonstrating its applicability in real-life situations.

At the present time, hand gestures recognition system could be used as a more\nexpected and useable approach for human computer interaction. Automatic hand\ngesture recognition system provides us a new tactic for interactive with the\nvirtual environment. In this paper, a face and hand gesture recognition system\nwhich is able to control computer media player is offered. Hand gesture and\nhuman face are the key element to interact with the smart system. We used the\nface recognition scheme for viewer verification and the hand gesture\nrecognition in mechanism of computer media player, for instance, volume\ndown/up, next music and etc. In the proposed technique, first, the hand gesture\nand face location is extracted from the main image by combination of skin and\ncascade detector and then is sent to recognition stage. In recognition stage,\nfirst, the threshold condition is inspected then the extracted face and gesture\nwill be recognized. In the result stage, the proposed technique is applied on\nthe video dataset and the high precision ratio acquired. Additional the\nrecommended hand gesture recognition method is applied on static American Sign\nLanguage (ASL) database and the correctness rate achieved nearby 99.40%. also\nthe planned method could be used in gesture based computer games and virtual\nreality.\n

The barrier between the hearing and the deaf communities in Kenya is a major challenge leading to a major gap in the communication sector where the deaf community is left out leading to inequality. The study used primary and secondary data sources to obtain information about this problem, which included online books, articles, conference materials, research reports, and journals on sign language and hand gesture recognition systems. To tackle the problem, CNN was used. Naturally captured hand gesture images were converted into grayscale and used to train a classification model that is able to identify the English alphabets from A-Z. Then identified letters are used to construct sentences. This will be the first step into breaking the communication barrier and the inequality. A sign language recognition model will assist in bridging the exchange of information between the deaf and hearing people in Kenya. The model was trained and tested on various matrices where we achieved an accuracy score of a 99% value when run on epoch of 10, the log loss metric returning a value of 0 meaning that it predicts the actual hand gesture images. The AUC and ROC curves achieved a 0.99 value which is excellent.

Remote controls are commonly used today as an interface for almost all consumer electronic equipment. Different home appliances use a multitude of remote controllers, offering a variety of problems: confusion in the different button layout, spending time in looking for the right remote, problems in replacement when destroyed and so on. Hand gestures have been one of the oldest and natural methods of human interaction. It is considered as an interaction medium that can possibly deliver more intuitive, creative and natural method for communication between human and a machine. The researchers choose to create an android based hand gesture controlled interface system for home appliances that serves as an alternative to existing remote controllers. The camera of the android device will be used to detect static hand gestures for processing. The gathered data will be compared to a database of recognizable static hand gestures to execute commands on certain home appliances using infrared light. After series of testing and evaluation, the system is able to execute control functions to the home appliances indicated by specific hand gestures. The system required correct execution of the corresponding static gesture to properly execute the command functions of the appliances with proper lighting and illuminance for proper recognition else it would result into loss of accuracy and false detection. For the system to be able to communicate or receive information the transmission of the sender (IR) should be in line of sight to the receivers (IR) with six (6) meters range between the appliances and the mobile phone. A 100% success rate of gesture recognition was observed in Samsung Galaxy S4 with 2.26451 seconds recognition time while there were only three failures of hand gesture detection using Samsung Galaxy Note 3 with 2.6991 seconds recognition time.

In this project we've discussed a system which uses some dynamic hand gesture recognition technique to manage the media players like VLC media player. This project consist of certain modules which segments the foreground a part of the frame using detection of skin colour and approximate median technique. the popularity of hand gesture is finished by creating a call Tree, that uses certain features extracted from the segmented part. This hand gesture recognition technique introduces a replacement, natural thanks to interact with computers and is beneficial to several folks in our day-to-day life. Hand gestures are associated with the human hands in hand gesture recognition. This project is employed for controlling certain operations (Pause, Play, Volume up, Volume Down, Mute) on video player by mere hand gestures without getting in the rigmarole of pressing buttons or tapping onto the screen. this may be directly associated with our day to life like in presentations. We have considered hand gestures and their directional motion defines a gesture for the applying. during this application image retrieving is finished employing a Webcam. Some functions in VLC media players are used most often and these functions uses some predefined gestures. Shows the defined gestures in keeping with the VLC player control operation. We created a VLC Media Player Controller using Hand Gesture Recognition System to form ‘HUMAN LIFE EASY AND BETTER’. This project is implemented using two steps: (1.) Creation of Hand Gesture Recognition System: this is often done using image processing using OpenCV library. (2.) Controlling VLC Media Player using hand gestures: during this step we controlled the player using shell commands which were recalled using python commands through OS library

DeReFNet: Dual-stream Dense Residual Fusion Network for static hand gesture recognition

Researchers have recently focused their attention on vision-based hand gesture recognition. However, due to several constraints, achieving an effective vision-driven hand gesture recognition system in real time has remained a challenge. This paper aims to uncover the limitations faced in image acquisition through the use of cameras, image segmentation and tracking, feature extraction, and gesture classification stages of vision-driven hand gesture recognition in various camera orientations. This paper looked at research on vision-based hand gesture recognition systems from 2012 to 2022. Its goal is to find areas that are getting better and those that need more work. We used specific keywords to find 108 articles in well-known online databases. In this article, we put together a collection of the most notable research works related to gesture recognition. We suggest different categories for gesture recognition-related research with subcategories to create a valuable resource in this domain. We summarize and analyze the methodologies in tabular form. After comparing similar types of methodologies in the gesture recognition field, we have drawn conclusions based on our findings. Our research also looked at how well the vision-based system recognized hand gestures in terms of recognition accuracy. There is a wide variation in identification accuracy, from 68% to 97%, with the average being 86.6 percent. The limitations considered comprise multiple text and interpretations of gestures and complex non-rigid hand characteristics. In comparison to current research, this paper is unique in that it discusses all types of gesture recognition techniques.

Indian sign language (ISL) is used by deaf people in India for their internal communication. Sign language recognition (SLR) system recognizes gestures of sign language and converts them into text and voice, thus enabling deaf people to communicate with others. In this paper, we have presented an automatic system for ISL hand gesture recognition. Modern features like Histogram of Oriented Gradient (HOG), Speeded Up Robust Features (SURF), Fourier Descriptors, Hu Moments, and Zernike Moments have been extracted from training images of our own created dataset. Using these features, most commonly used classifiers like Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) have been trained and evaluated. Comparative performance analysis of these classifiers along with experimental results is the main topic of discussion included in this paper. Moreover, a user-friendly GUI of the system allows user to select any combination of features and classifiers to perform gesture recognition from both stored file and live camera. During experiment, it is found that the system is able to recognize gestures with a recognition rate up to 98.70%.