Comparison of hand segmentation methodologies for Hand gesture recognition

Human Computer Interaction (HCI) has become very important in today's world. Existing HCI techniques like keyboard, mouse, joysticks etc. limit the speed and naturalness of our interaction. Hand gestures provide a more natural way of communicating with the computer. Hand segmentation is the most crucial step in every hand gesture recognition system. All subsequent steps greatly depend on the quality of segmentation. If data is lost due to improper segmentation, it can hardly be recovered in the later stages of gesture recognition process. In this paper, we present a robust and effective method of hand segmentation which overcomes problems such as skin color detection, complex background removal, complexity from multiple gesturers in front of the camera and variable lighting condition.

MuViH: Multi-View Hand gesture dataset and recognition pipeline for human–robot interaction in a collaborative robotic finishing platform

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.

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.

Hand gesture recognition is a quickly developing field with many uses in human-computer interaction, sign language recognition, virtual reality, gaming, and robotics. This paper reviews different ways to model hands, such as vision-based, sensor-based, and data glove-based techniques. It emphasizes the importance of accurate hand modeling and feature extraction for capturing and analyzing gestures. Key features like motion, depth, color, shape, and pixel values and their relevance in gesture recognition are discussed. Challenges faced in hand gesture recognition include lighting variations, complex backgrounds, noise, and real-time performance. Machine learning algorithms are used to classify and recognize gestures based on extracted features. The paper emphasizes the need for further research and advancements to improve hand gesture recognition systems' robustness, accuracy, and usability. This review offers valuable insights into the current state of hand gesture recognition, its applications, and its potential to revolutionize human-computer interaction and enable natural and intuitive interactions between humans and machines. In simpler terms, hand gesture recognition is a way for computers to understand what people are saying with their hands. It has many potential applications, such as allowing people to control computers without touching them or helping people with disabilities communicate. The paper reviews different ways to develop hand gesture recognition systems and discusses the challenges and opportunities in this area.

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

Hand gesture recognition is a relevant study topic for a reason that sometimes we may not be in position to communicate verbally. There is need to design Hand gesture recognition systems in order to help people adopt to nonverbal communication mainly sign language. However, there is no clue to understand the meaning of gesture through the computers directly. So this calls for definitions that generalize models in a computer. That is why the machine-learning approaches are implemented in recognition systems. There are generally two types of hand gestures recognition systems which researches have concentrated on. These include static and dynamic Hand gesture recognition systems. However, in building Hand gesture recognition systems, various machine learning approaches have been used. For implementing the proposed system, MS Kinect depth sensor was used as a hardware. The Kinect depth sensor is composed of an infrared camera. This is an advantage to the systems that are designed basing on the depth sensing because factors like color, clothing and background have less effect on the performance. So Kinect based depth sensor systems have a high accuracy and performance making them relevant and applicable in our daily lives. In this paper, we propose a static hand gesture recognition system in real time using two machine learning methods namely Support Vector Machine and Artificial Neural Networks. We use of the newly launched Microsoft Kinect sensor for image extraction. The sensor helps us to extract the hand images. We implement the system on a Matlab platform for reasons that Matlab is widely used by researchers in different fields and that can handle complex computations. In the training of the model, we collect a hundred depth-based Histogram of Oriented Gradient features per alphabet from the hand gesture images which we trained, tested and validated using Artificial Neural Networks (ANN) and Support Vector Machine (SVM). From this dataset, we can generate the generalized gesture model for each alphabet image. For the proposed system, the classification with ANN proves a higher performance then SVM.

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

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.

With the escalating role of computers in educational system, human computer interaction, is becoming gradually more important part of it. The general believe is that with the progress in computing speed, communication technologies, and display techniques the existing HCI techniques may become a constraint in the effectual utilization of the existing information flow. The development of user interfaces influences the changes in the Human-Computer Interaction (HCI). Human hand gestures have been a mode of non verbal interaction widely used. The vocabulary of hand gesture communication has many variations. It ranges from simple action of using our finger to point at and using hands to move objects around for more complex expressions for the feelings and communicating with others. Also the hand gestures play a prominent role in teaching considering the explanations and exemplifications being highly dependent on hand gestures. Naturalistic and intuitiveness of the hand gesture has been an immense motivating aspect for the researchers in the field of Human Computer Interaction to put their efforts to research and develop the more promising means of interaction involving human and computers. The pursuance for the Human Computer Interaction research is moved by the central dogma of removing the complex and cumbersome interaction devices and replacing them with more obvious and expressive means of interaction which easily comes to the users with least cognitive burden like hand gestures. This paper designs a simple, natural system for gestural interaction between the user and computer for providing a dynamic user interface. The gesture recognition system uses image processing techniques for detection, segmentation, tracking and recognition of hand gestures for converting it to a meaningful command. This hand gesture recognition system has been proposed, designed and developed with the intensions to make it a substitute for mouse while making dynamic user interface between human and machine. Hence instead of making effort to develop a new vocabulary of hand gesture we have matched control instruction set of mouse to subset of most discriminating hand gestures, so that we get a robust interface. The interface being proposed here can be substantially applied towards different applications like image browser, games etc.

Hand gesture recognition system can be used for human-computer interaction (HCI). Proper hand segmentation from the background and other body parts of the video is the primary requirement for the design of a hand-gesture based application. These video frames can be captured from a low cost webcam (camera) for use in a vision based gesture recognition technique. This paper discusses about the continuous hand gesture recognition. The aim of this paper is to report a robust and efficient hand segmentation algorithm where a new method, wearing glove on the hand is utilized. After that a new idea called “Finger-Pen”, is developed by segmenting only one finger from the hand for proper tracking. In this technique only a finger tip is segmented in spite of the full hand part. Hence this technique allows the hand (excepting the segmented finger tip) to move freely during the tracking time also. Problems such as skin colour detection, complexity from large numbers of people in front of the camera, complex background removal and variable lighting condition are found to be efficiently handled by the system. Noise present in the segmented image due to dynamic background can be removed with the help of this adaptive technique which is found to be effective for the application conceived.

In recent years, hand gesture recognition (HGR) systems have been extensively developed with the technologies of human-computer interaction (HCI), which enables regular interaction with machines. However, the significance of progress in HGR continues to advance, although the problem of hand segmentation and recognition is challenging due to the unfavorable environment, background illumination, hand size, and shape. To overcome this, we propose an optimal segmentation method for identifying hand gestures from input images, improving recognition performance. For segmenting hand gestures, we compared the segmentation methods of YCbCr, SkinMask, and HSV (hue, saturation, and value). The CR component is extracted from YCbCr, then binarization, erosion, and hole filling are performed. Color segmentation is applied to the SkinMask process that detects pixels that match the color of the hand. In the HSV process, threshold masking determines the dominant features. The Softmax classification is used to classify hand gestures where features are extracted through convolutional neural network (CNN). The proposed segmentation methods are applied to a benchmark dataset and the result shows an improvement in recognition accuracy over state-of-the-art systems.

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.

Hand gesture recognition is an important area of research in the field of Human Computer Interaction (HCI). The geometric attributes of the hand play an important role in hand shape reconstruction and gesture recognition. That said, fingertips are one of the important attributes for the detection of hand gestures and can provide valuable information from hand images. Many methods are available in scientific literature for fingertips detection with an open hand but very poor results are available for fingertips detection when the hand is closed. This paper presents a new method for the detection of fingertips in a closed hand using the corner detection method and an advanced edge detection algorithm. It is important to note that the skin color segmentation methodology did not work for fingertips detection in a closed hand. Thus the proposed method applied Gabor filter techniques for the detection of edges and then applied the corner detection algorithm for the detection of fingertips through the edges. To check the accuracy of the method, this method was tested on a vast number of images taken with a webcam. The method resulted in a higher accuracy rate of detections from the images. The method was further implemented on video for testing its validity on real time image capturing. These closed hand fingertips detection would help in controlling an electro-mechanical robotic hand via hand gesture in a natural way.

The hand gesture provides a natural and intuitive communication medium for the human and machine interaction. Because, it can use in virtual reality, language detection, computer games, and other human-computer or human-machine instruction applications. Currently, the sensor and camera-based application is a field of interest for many researchers. This paper proposes a new hand gesture recognition system using the Kinect sensor's skeleton data, which works in an environment where people do not touch devices or communicate verbally. The proposed model focuses on mainly two modules, namely, hand area and fingertip detection, and hand gesture recognition. The hand area and fingertip are detected by positioning the palm point and find extreme of contour. And, the hand gesture is recognized by measuring the distance between different body indexes of skeleton information. Here, six gestures instructions are considered such as move right to left, move left to right, move up to down, move down to up, open and closed, and also recognize the numeric number using the fingertip. This system is able to detect the presence of hand area and fingers and to recognize different hand gestures. As a result, the average recognition accuracy of different hand gestures and stretched fingers numbers are 95.91% and 96%, respectively.