A novel hand segmentation method for multiple-hand gesture recognition system under complex background

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.

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.

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.

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.

The Hand gesture has provided significant means of communication in human daily interaction and has been widely explored in the Human-Computer Interaction (HCI) studies. This paper presents a comparison of hand segmentation methodologies in the early stage development of an appearance-based hand gesture recognition system for sign language application. The hand segmentation method is based on Jones and Rehg generic skin color model and frame differencing technique that utilized the color and motion cues of image content. Several issues and challenges occurred during the experiments are also discussed and later tackled by the current approach. The present approach applied the idea of integrating both color and motion cues into a single probability map and yield robust features for further tasks. It is merely a first step progress towards an independent environment and signer for the hand gesture recognition system.

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.

Since the 1970s, the field of gesture recognition and its applications has been at the centre of considerable research in human–computer interaction. Researchers have been able to construct strong models that can recognize gestures in real time thanks to recent advances in deep learning and computer vision, but they face hurdles when it comes to classifying gestures in variable lighting conditions. In this paper, we train a 3D convolutional neural network to recognize dynamic hand gestures in real time. Our focus is on ensuring that gesture recognition systems can perform well under varying light conditions. We use a huge training set consisting of numerous clips of people performing specific gestures in varying lighting conditions to train the model. We were able to attain an accuracy of 76.40% on the training set and 66.56% on the validation set with minimal pre-processing applied to the data set. The trained model was able to successfully recognize hand gestures recorded from a Webcam in real time. We were then able to use the model’s predictions to control video playback on the VLC media player such as increasing/decreasing volume and pausing video. These experimental results show the effectiveness and efficiency of the proposed framework to recognize gestures in both bright and dim lighting conditions.KeywordsDeep learningConvolutional neural networksPyTorch3D CNN

Urban intelligence is an emerging concept which guides a series of infrastructure developments in modern smart cities. Human-computer interaction (HCI) is the interface between residents and the smart cities, it plays a key role in bridging the gap in applicating information technologies in modern cities. Hand gestures have been widely acknowledged as a promising HCI method, recognition human hand gestures using surface electromyogram (sEMG) is an important research topic in the application of sEMG. However, state-of-the-art signal processing technologies are not robust in feature extraction and pattern recognition with sEMG signals, several technical problems are still yet to be solved. For example, how to maintain the availability of myoelectric control in intermittent use, since pattern recognition qualities are greatly affected by time variability, but it is unavoidable during daily use. How to ensure the reliability and effectiveness of myoelectric control system also important in developing a good human-machine interface. In this paper, linear discriminant analysis (LDA) and extreme learning machine (ELM) are implemented in hand gesture recognition system, which is able to reduce the redundant information in sEMG signals and improve recognition efficiency and accuracy. The characteristic map slope (CMS) is extracted by using the feature re-extraction method because CMS can strengthen the relationship of features cross time domain and enhance the feasibility of cross-time identification. This study is focusing on optimizing the time differences in sEMG pattern recognition, the experimental results are beneficial to reducing the time differences in gesture recognition based on sEMG. The recognition framework proposed in this paper can enhance the generalization ability of HCI in the long term use and it also simplifies the data collection stage before training the device ready for daily use, which is of great significance to improve the time generalization performance of an HCI system.

Tracking moving objects is an area increasingly known in computer vision field. It plays a very important role in human-computer interaction. In this context we have developed a hand tracking and gesture recognition system that allows interaction with the machine in an intuitive and natural way. To ensure the tracking we apply the Kalman filter and detect the optimal points of the hand in order to determine the gesture expressed by user.

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

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.

Human-computer interaction introduces critical open door with the proceeds with improvement of wearable gadgets. Gesture recognition through smart devices is becoming a popular research direction. This paper proposes a hand gesture recognition and real-time game control system that is capable of continues human-computer interaction in view of an off-the-rack business wearable wristband. We utilize three-axis accelerator and gyroscope sensors embedded in smart band to collect hand motion information and use Kinect camera capture video information for manual segmentation during the training model phase. A continuous gesture segmentation algorithm based on sliding window and DTW algorithm is developed to detect meaningful gestures in the real-time game control stage. In addition, an android game named Fly Birds which is controlled by gesture recognition result is presented to simulate real-time human-computer interaction. Then, we classify the data in the window using common classifiers. Finally, our experimental results show that, we can accurately identify the designed gestures during the stage of static gesture recognition, and we also achieve a perfect interactive effect in the process of dynamic real-time game control. The experiment outcomes will advance the ascent of human-PC cooperation in view of hand gesture recognition and related applications will rise in vast numbers.

Hand motion tracking and gesture recognition are a fundamental technology in the field of proactive computing for a better human computer interaction system. In this paper, we have developed a 3-D hand motion tracking and gesture recognition system via a data glove (namely the KHU-1 data glove consisting of three tri-axis accelerometer sensors, one controller, and one Bluetooth). The KHU-1 data glove is capable of transmitting hand motion signals to a PC through wireless communication via Bluetooth. Also we have implemented a 3-D digital hand model for hand motion tracking and recognition. The implemented 3-D digital hand model is based on the kinematic chain theory utilizing ellipsoids and joints. Finally, we have utilized a rule-based algorithm to recognize simple hand gestures namely scissor, rock, and paper using the 3-D digital hand model and the KHU-1 data glove. Some preliminary experimental results are presented in this paper.

This paper presents head and hand gestures recognition system for Human Computer Interaction (HCI). Head and Hand gestures are an important modality for human computer interaction. Vision based recognition system can give computers the capability of understanding and responding to the hand and head gestures. The aim of this paper is the proposal of real time vision system for its application within a multimedia interaction environment. This recognition system consists of four modules, i.e. capturing the image, image extraction, pattern matching and command determination. If hand and head gestures are shown in front of the camera, hardware will perform respective action. Gestures are matched with the stored database of gestures using pattern matching. Corresponding to matched gesture, the hardware is moved in left, right, forward and backward directions. An algorithm for optimizing connected component in gesture recognition is proposed, which makes use of segmentation in two images. Connected component algorithm scans an image and group its pixels into component based on pixel connectivity i.e. all pixels in connected component share similar pixel intensity values and are in some way connected with each other. Once all groups have been determined, each pixel is labeled with a color according to component it was assigned to.

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.