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Journal articles on the topic 'Vision based hand gesture recognition'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Lou, Xinyue. "Vision-based Hand Gesture Recognition Technology." Applied and Computational Engineering 141, no. 1 (2025): 54–59. https://doi.org/10.54254/2755-2721/2025.21696.

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Human-computer interaction has a wide range of application prospects in many fields such as medicine, entertainment, industry and education. Gesture recognition is one of the most important technologies for gesture interaction between humans and robots, and visual gesture recognition increases the user's comfort and freedom compared with data glove recognition. This paper summarizes the general process of visual gesture recognition based on the literature, including three steps: pre-processing, feature extraction, and gesture classification. It also defines static and dynamic gestures and make

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Jasim, Mahmood, Tao Zhang, and Md Hasanuzzaman. "A Real-Time Computer Vision-Based Static and Dynamic Hand Gesture Recognition System." International Journal of Image and Graphics 14, no. 01n02 (2014): 1450006. http://dx.doi.org/10.1142/s0219467814500065.

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This paper presents a novel method for computer vision-based static and dynamic hand gesture recognition. Haar-like feature-based cascaded classifier is used for hand area segmentation. Static hand gestures are recognized using linear discriminant analysis (LDA) and local binary pattern (LBP)-based feature extraction methods. Static hand gestures are classified using nearest neighbor (NN) algorithm. Dynamic hand gestures are recognized using the novel text-based principal directional features (PDFs), which are generated from the segmented image sequences. Longest common subsequence (LCS) algor

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Bhumkar, Prathamesh. "HAND GESTURE CONTROLLER." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem35055.

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In this paper we present an interaction between humans and computer, gesture recognition does play a critical role. While technology has developed to such a level it made possible to communicate with computers with the Gesture Recognition system. Having reached all the best possible ways for data acquisition like cameras, hand Movement now these are of less concern. The desire for human-machine interaction is rapidly growing due to advancements in computer vision technology. Gesture recognition is used extensively in many different types of fields. It indicates that research into vision-based

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RAUTARAY, SIDDHARTH S., and ANUPAM AGRAWAL. "VISION-BASED APPLICATION-ADAPTIVE HAND GESTURE RECOGNITION SYSTEM." International Journal of Information Acquisition 09, no. 01 (2013): 1350007. http://dx.doi.org/10.1142/s0219878913500071.

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With the increasing role of computing devices, facilitating natural human computer interaction (HCI) will have a positive impact on their usage and acceptance as a whole. For long time, research on HCI has been restricted to techniques based on the use of keyboard, mouse, etc. Recently, this paradigm has changed. Techniques such as vision, sound, speech recognition allow for much richer form of interaction between the user and machine. The emphasis is to provide a natural form of interface for interaction. Gestures are one of the natural forms of interaction between humans. As gesture commands

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Gupta, Himanshu, Aniruddh Ramjiwal, and Jasmin T. Jose. "Vision Based Approach to Sign Language Recognition." International Journal of Advances in Applied Sciences 7, no. 2 (2018): 156. http://dx.doi.org/10.11591/ijaas.v7.i2.pp156-161.

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We propose an algorithm for automatically recognizing some certain amount of gestures from hand movements to help deaf and dumb and hard hearing people. Hand gesture recognition is quite a challenging problem in its form. We have considered a fixed set of manual commands and a specific environment, and develop a effective, procedure for gesture recognition. Our approach contains steps for segmenting the hand region, locating the fingers, and finally classifying the gesture which in general terms means detecting, tracking and recognising. The algorithm is non-changing to rotations, translations

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Himanshu, Gupta, Ramjiwal Aniruddh, and T. Jose Jasmin. "Vision Based Approach to Sign Language Recognition." International Journal of Advances in Applied Sciences (IJAAS) 7, no. 2 (2018): 156–61. https://doi.org/10.11591/ijaas.v7.i2.pp156-161.

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We propose an algorithm for automatically recognizing some certain amount of gestures from hand movements to help deaf and dumb and hard hearing people. Hand gesture recognition is quite a challenging problem in its form. We have considered a fixed set of manual commands and a specific environment, and develop a effective, procedure for gesture recognition. Our approach contains steps for segmenting the hand region, locating the fingers, and finally classifying the gesture which in general terms means detecting, tracking and recognising. The algorithm is non-changing to rotations, translations

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N. Balaji, G., S. V. Suryanarayana, and C. Veeramani. "Invariant Hand Gesture Recognition System." International Journal of Engineering & Technology 7, no. 4.6 (2018): 299. http://dx.doi.org/10.14419/ijet.v7i4.6.20717.

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Hand gesture recognition plays a vital role in numerous applications, which can run from mobile phones to 3D analysis of anatomy and from gaming to medicinal science. In a large portion of research applications and current business hand gestures recognition, has been implemented by utilizing either vision based or sensor-based gloves strategies where hues, paperclips of synthetic substances are used on to capture the gestures. Another essential issue associated with vision-based procedures is illuminated conditions. The threshold used for the segmentation is changed based on the light variatio

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N. Balaji, G., S. V. Suryanarayana, and C. Veeramani. "Invariant Hand Gesture Recognition System." International Journal of Engineering & Technology 7, no. 4.6 (2018): 299. http://dx.doi.org/10.14419/ijet.v7i4.6.21196.

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Hand gesture recognition plays a vital role in numerous applications, which can run from mobile phones to 3D analysis of anatomy and from gaming to medicinal science. In a large portion of research applications and current business hand gestures recognition, has been implemented by utilizing either vision based or sensor-based gloves strategies where hues, paperclips of synthetic substances are used on to capture the gestures. Another essential issue associated with vision-based procedures is illuminated conditions. The threshold used for the segmentation is changed based on the light variatio

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Narayanpethkar, Sangamesh. "Computer Vision based Media Control using Hand Gestures." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 6642–46. http://dx.doi.org/10.22214/ijraset.2023.52881.

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Abstract: Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. At this time and age, working with a computer in some capacity is a common task. In most situations, the keyboard and mouse are the primary input devices. However, there are several problems associated with excessive usage of the same interaction medium, such as health problems brought on by continuous use of input devices, etc. Humans basically communicate usin

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Kotavenuka, Swetha, Harshitha Kodakandla, Nimmakayala Sai Krishna, and Dr S. P. V. Subba Rao. "Hand Gesture Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 331–35. http://dx.doi.org/10.22214/ijraset.2023.48557.

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Abstract: This work presents a computer-vision-based application for recognizing hand gestures. A live video feed is captured by a camera, and a still image is extracted from that feed with the aid of an interface. At least once per count hand gesture (one, two, three, four, and five), the system is trained. After that, the system is given a test gesture to see if it can identify it. Several algorithms that are capable of distinguishing a hand gesture were studied. It was determined that the highest rate of accuracy was achieved by using the computational neural network known as the Alexnet al

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Nyirarugira, Clementine, Hyo-rim Choi, and TaeYong Kim. "Hand Gesture Recognition Using Particle Swarm Movement." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/1919824.

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We present a gesture recognition method derived from particle swarm movement for free-air hand gesture recognition. Online gesture recognition remains a difficult problem due to uncertainty in vision-based gesture boundary detection methods. We suggest an automated process of segmenting meaningful gesture trajectories based on particle swarm movement. A subgesture detection and reasoning method is incorporated in the proposed recognizer to avoid premature gesture spotting. Evaluation of the proposed method shows promising recognition results: 97.6% on preisolated gestures, 94.9% on stream gest

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Yang, Lewei. "Real-time gesture-based control of UAVs using multimodal fusion of FMCW radar and vision." Journal of Physics: Conference Series 2664, no. 1 (2023): 012002. http://dx.doi.org/10.1088/1742-6596/2664/1/012002.

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Abstract Gesture-based control has gained prominence as an intuitive and natural means of interaction with unmanned aerial vehicles (UAVs). This paper presents a real-time gesture-based control system for UAVs that leverages the multimodal fusion of Frequency Modulated Continuous Wave (FMCW) radar and vision sensors, aiming to enhance user experience through precise and responsive UAV control via hand gestures. The research focuses on developing an effective fusion framework that combines the complementary advantages of FMCW radar and vision sensors. FMCW radar provides robust range and veloci

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Kolhe, Ashwini, R. R. Itkarkar, and Anilkumar V. Nandani. "Robust Part-Based Hand Gesture Recognition Using Finger-Earth Mover’s Distance." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (2017): 131. http://dx.doi.org/10.23956/ijarcsse/v7i7/0196.

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Hand gesture recognition is of great importance for human-computer interaction (HCI), because of its extensive applications in virtual reality, sign language recognition, and computer games. Despite lots of previous work, traditional vision-based hand gesture recognition methods are still far from satisfactory for real-life applications. Because of the nature of optical sensing, the quality of the captured images is sensitive to lighting conditions and cluttered backgrounds, thus optical sensor based methods are usually unable to detect and track the hands robustly, which largely affects the p

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Naveen, Y., and Ch Navya Sree. "GestureFlow: Advanced Hand Gesture Control System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44540.

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Our project "GestureFlow: Advanced Hand Gesture Control System" leverages real-time computer vision and deep learning techniques to create a robust, touchless control interface using hand gestures. The system utilizes MediaPipe Hands for efficient hand landmark detection and processes dynamic hand movements and finger configurations to identify a wide range of intuitive gestures such as swipes, pinches, and specific finger patterns. These gestures are mapped to actions like mouse control, clicks, volume adjustment, media playback, screenshot capture, window management, and many more. The syste

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Yong Xu. "Research on Dynamic Gesture Recognition and Control System based on Machine Vision." Journal of Electrical Systems 20, no. 2 (2024): 616–28. http://dx.doi.org/10.52783/jes.1215.

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Hand gesture recognition and control is a new type of human-computer interaction that can provide a more convenient and efficient operation mode by utilizing non-contact gesture recognition technology. This paper presents a lightweight dynamic gesture recognition method for intelligent office presentation control. First, we introduce the concept of hand gesture recognition and go over key gesture recognition technologies like classification. The structure, process, and evaluation index of the gesture recognition algorithm are described in detail using a convolutional neural network model. Duri

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Venkateswarlu, Dr S. China. "Convolutional Neural Network for Hand Gesture Recognition Using 8 Different Gestures." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48820.

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Abstract -- Hand gesture is the main method of communication for people who are hearing-impaired, which poses a difficulty for millions of individuals worldwide when engaging with those who do not have hearing impairments. The significance of technology in enhancing accessibility and thereby increasing the quality of life for individuals with hearing impairments is universally recognized. Therefore, this study conducts a systematic review of existing literature review on hand gesture recognition, with a particular focus on existing methods that address the application of vision, sensor, and hy

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Kareem Murad, Bothina, and Abbas H. Hassin Alasadi. "Advancements and Challenges in Hand Gesture Recognition: A Comprehensive Review." Iraqi Journal for Electrical and Electronic Engineering 20, no. 2 (2024): 154–64. http://dx.doi.org/10.37917/ijeee.20.2.13.

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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, co

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Oudah, Munir, Ali Al-Naji, and Javaan Chahl. "Hand Gesture Recognition Based on Computer Vision: A Review of Techniques." Journal of Imaging 6, no. 8 (2020): 73. http://dx.doi.org/10.3390/jimaging6080073.

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Hand gestures are a form of nonverbal communication that can be used in several fields such as communication between deaf-mute people, robot control, human–computer interaction (HCI), home automation and medical applications. Research papers based on hand gestures have adopted many different techniques, including those based on instrumented sensor technology and computer vision. In other words, the hand sign can be classified under many headings, such as posture and gesture, as well as dynamic and static, or a hybrid of the two. This paper focuses on a review of the literature on hand gesture

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Wang, Huihui, Bo Ru, Xin Miao, et al. "MEMS Devices-Based Hand Gesture Recognition via Wearable Computing." Micromachines 14, no. 5 (2023): 947. http://dx.doi.org/10.3390/mi14050947.

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Gesture recognition has found widespread applications in various fields, such as virtual reality, medical diagnosis, and robot interaction. The existing mainstream gesture-recognition methods are primarily divided into two categories: inertial-sensor-based and camera-vision-based methods. However, optical detection still has limitations such as reflection and occlusion. In this paper, we investigate static and dynamic gesture-recognition methods based on miniature inertial sensors. Hand-gesture data are obtained through a data glove and preprocessed using Butterworth low-pass filtering and nor

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Jahnavi, Mudili. "Controlling Computer Using Hand Gestures." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49260.

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Abstract: In the realm of Human-Computer Interaction (HCI), the integration of webcams and various sensors has made gesture recognition increasingly accessible and impactful. Hand gestures provide a natural and intuitive mode of communication, enabling seamless interaction between humans and computers. This paper highlights the potential of hand gestures as an effective medium for non-verbal communication and control, with applications spanning across multiple domains. The proposed system leverages image processing techniques, sensor technologies, and computer vision to enable gesture-based co

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Yaseen, Oh-Jin Kwon, Jaeho Kim, Jinhee Lee, and Faiz Ullah. "Vision-Based Gesture-Driven Drone Control in a Metaverse-Inspired 3D Simulation Environment." Drones 9, no. 2 (2025): 92. https://doi.org/10.3390/drones9020092.

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Unlike traditional remote control systems for controlling unmanned aerial vehicles (UAVs) and drones, active research is being carried out in the domain of vision-based hand gesture recognition systems for drone control. However, contrary to static and sensor based hand gesture recognition, recognizing dynamic hand gestures is challenging due to the complex nature of multi-dimensional hand gesture data, present in 2D images. In a real-time application scenario, performance and safety is crucial. Therefore we propose a hybrid lightweight dynamic hand gesture recognition system and a 3D simulato

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Tasfia, Rifa, Mohd Yusoh Zeratul Izzah, Habib Adria Binte, and Tousif Mohaimen. "An overview of hand gesture recognition based on computer vision." An overview of hand gesture recognition based on computer vision 14, no. 4 (2024): 4636–45. https://doi.org/10.11591/ijece.v14i4.pp4636-4645.

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Hand gesture recognition emerges as one of the foremost sectors which has gone through several developments within pattern recognition. Numerous studies and research endeavors have explored methodologies grounded in computer vision within this domain. Despite extensive research endeavors, there is still a need for a more thorough evaluation of the efficiency of various methods in different environments along with the challenges encountered during the application of these methods. The focal point of this paper is the comparison of different research in the dom

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Shah, Pranit, Krishna Pandya, Harsh Shah, and Jay Gandhi. "Survey on Vision based Hand Gesture Recognition." International Journal of Computer Sciences and Engineering 7, no. 5 (2019): 281–88. http://dx.doi.org/10.26438/ijcse/v7i5.281288.

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Raheem, S. Abdul, A. Shiva Sai, B. Keerthana, and Asst Prof Mrs A. Amulya. "Vision and Voice Based Hand Gesture Recognition." International Journal for Research in Applied Science and Engineering Technology 11, no. 6 (2023): 690–94. http://dx.doi.org/10.22214/ijraset.2023.53731.

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Abstract: The development of sign language over time has been astounding. It's unfortunate that this language has some unpleasant side effects. Not everyone can understand spoken sign language when using sign language among a mute or deaf person. For deaf-dumb people, hand gestures and sign language are important forms of communication. Communication is difficult without an interpreter, hence it is necessary to translate sign language so that it is understandable to the general public. The goal is to increase the participation of the deaf and the mute in communication. In order to incorporate

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Satybaldina, Dina, and Gulzia Kalymova. "Deep learning based static hand gesture recognition." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 398. http://dx.doi.org/10.11591/ijeecs.v21.i1.pp398-405.

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Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human–computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For pre-processing and

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Satybaldina, Dina, and Gulzia Kalymova. "Deep learning based static hand gesture recognition." Indonesian Journal of Electrical Engineering and Computer Science 21, no. 1 (2021): 398–405. https://doi.org/10.11591/ijeecs.v21.i1.pp398-405.

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Hand gesture recognition becomes a popular topic of deep learning and provides many application fields for bridging the human-computer barrier and has a positive impact on our daily life. The primary idea of our project is a static gesture acquisition from depth camera and to process the input images to train the deep convolutional neural network pre-trained on ImageNet dataset. Proposed system consists of gesture capture device (Intel® RealSense™ depth camera D435), pre-processing and image segmentation algorithms, feature extraction algorithm and object classification. For preproce

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Hu, Hong, Jian Gang Chao, and Zai Qian Zhao. "Study of Vision-Based Hand Gesture Recognition System for Astronaut Virtual Training." Advanced Materials Research 998-999 (July 2014): 1062–65. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.1062.

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With the fast development of vision-based hand gesture recognition, it is possible to apply the technology to astronaut virtual training. In order to solve problems of hand gesture recognition in future virtual training and to provide an unrestricted natural training for astronauts, this paper proposed a vision-based hand gesture recognition method, and implemented a hierarchical gesture recognition system to provide a gesture-driven interactive interface for astronaut virtual training system. The experiment results showed that this recognition system can be used to help astronaut training.

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Jhaung, Yu-Chiao, Yu-Ming Lin, Chiao Zha, Jenq-Shiou Leu, and Mario Köppen. "Implementing a Hand Gesture Recognition System Based on Range-Doppler Map." Sensors 22, no. 11 (2022): 4260. http://dx.doi.org/10.3390/s22114260.

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There have been several studies of hand gesture recognition for human–machine interfaces. In the early work, most solutions were vision-based and usually had privacy problems that make them unusable in some scenarios. To address the privacy issues, more and more research on non-vision-based hand gesture recognition techniques has been proposed. This paper proposes a dynamic hand gesture system based on 60 GHz FMCW radar that can be used for contactless device control. In this paper, we receive the radar signals of hand gestures and transform them into human-understandable domains such as range

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Chen, Shang-Liang, and Li-Wu Huang. "Using Deep Learning Technology to Realize the Automatic Control Program of Robot Arm Based on Hand Gesture Recognition." International Journal of Engineering and Technology Innovation 11, no. 4 (2021): 241–50. http://dx.doi.org/10.46604/ijeti.2021.7342.

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In this study, the robot arm control, computer vision, and deep learning technologies are combined to realize an automatic control program. There are three functional modules in this program, i.e., the hand gesture recognition module, the robot arm control module, and the communication module. The hand gesture recognition module records the user’s hand gesture images to recognize the gestures’ features using the YOLOv4 algorithm. The recognition results are transmitted to the robot arm control module by the communication module. Finally, the received hand gesture commands are analyzed and exec

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Tasfia, Rifa, Zeratul Izzah Mohd Yusoh, Adria Binte Habib, and Tousif Mohaimen. "An overview of hand gesture recognition based on computer vision." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 4 (2024): 4636. http://dx.doi.org/10.11591/ijece.v14i4.pp4636-4645.

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Hand gesture recognition emerges as one of the foremost sectors which has gone through several developments within pattern recognition. Numerous studies and research endeavors have explored methodologies grounded in computer vision within this domain. Despite extensive research endeavors, there is still a need for a more thorough evaluation of the efficiency of various methods in different environments along with the challenges encountered during the application of these methods. The focal point of this paper is the comparison of different research in the domain of vision-based hand gesture re

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Padmaja, M. "Wave Your Way: Navigation Through Hand Gestures." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42033.

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Several technologies are continuously developing in today’s technological world where human-computer interaction is very important. In human-computer interactions, hand gesture recognition is essential. We can control our system by showing our hands in front of a webcam, and hand gesture recognition can be useful for all kinds of people. A specific interactive module like a virtual mouse that makes use of Object Tracking and Gestures will help us interact and serve as an alternative way to the traditional touchscreen and physical mouse. The system allows people to control a computer cursor usi

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Ramadhani, Arief, Achmad Rizal, and Erwin Susanto. "Development of Hand Gesture Based Electronic Key Using Microsoft Kinect." MATEC Web of Conferences 218 (2018): 02014. http://dx.doi.org/10.1051/matecconf/201821802014.

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Computer vision is one of the fields of research that can be applied in a various subject. One application of computer vision is the hand gesture recognition system. The hand gesture is one of the ways to interact with computers or machines. In this study, hand gesture recognition was used as a password for electronic key systems. The hand gesture recognition in this study utilized the depth sensor in Microsoft Kinect Xbox 360. Depth sensor captured the hand image and segmented using a threshold. By scanning each pixel, we detected the thumb and the number of other fingers that open. The hand

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M, Chandraman, Santhiyakumari N, Ganesh Venkateshwaran S, Damodharan M, Santhiya C, and Subalakshimi V. "EchoGesture Communication: Gesture-based Systems for Individuals with Disabilities." September 2024 6, no. 3 (2024): 253–61. http://dx.doi.org/10.36548/jei.2024.3.004.

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EchoGesture Communication revolutionizes the interaction of differently-abled individuals using hand gestures. People with disabilities often face difficulties in using the conventional electronic gadgets. The proposed study, utilizes sensors, microcontroller, computer vision, and machine learning, to enable real-time recognition of hand gestures, facilitating effective communication. Additionally, Convolutional Neural Network (CNN) is used in the research to achieve accurate gesture recognition. The proposed system allows individuals with disability to communicate effectively using hand gestu

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Sikri, Nidhi, and Navleen Singh Rekhi. "Vision Based Analysis of Hand Gesture Recognition for Human Computer Interaction (HCI)." International Journal of Scientific Engineering and Research 5, no. 7 (2017): 316–20. https://doi.org/10.70729/ijser171683.

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Yoo, Minjeong, Yuseung Na, Hamin Song, et al. "Motion Estimation and Hand Gesture Recognition-Based Human–UAV Interaction Approach in Real Time." Sensors 22, no. 7 (2022): 2513. http://dx.doi.org/10.3390/s22072513.

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As an alternative to traditional remote controller, research on vision-based hand gesture recognition is being actively conducted in the field of interaction between human and unmanned aerial vehicle (UAV). However, vision-based gesture system has a challenging problem in recognizing the motion of dynamic gesture because it is difficult to estimate the pose of multi-dimensional hand gestures in 2D images. This leads to complex algorithms, including tracking in addition to detection, to recognize dynamic gestures, but they are not suitable for human–UAV interaction (HUI) systems that require sa

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H T, Panduranga, and Mani C. "Non – Vision Based Sensors for Dynamic Hand Gesture Recognition Systems: A Comparative Study." International Journal of Engineering & Technology 7, no. 3.12 (2018): 1175. http://dx.doi.org/10.14419/ijet.v7i3.12.17782.

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Gestures are considered as a type of configuration associated with motion in concerned body part, signifying meaningful information or expressing motion or intending to command and control. Wide ranges of sensors working with different technology are available in market. Gesture recognition process involves steps like data acquisition from sensor, segmentation, an algorithm for taking gesture data as input, an algorithm to extract parameters and algorithm to classify hand gestures. Three - dimensional hand gestures have been widely accepted for advanced applications like creation of virtual wo

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Jiang, Hairong, Juan P. Wachs, and Bradley S. Duerstock. "Integrated vision-based system for efficient, semi-automated control of a robotic manipulator." International Journal of Intelligent Computing and Cybernetics 7, no. 3 (2014): 253–66. http://dx.doi.org/10.1108/ijicc-09-2013-0042.

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Purpose – The purpose of this paper is to develop an integrated, computer vision-based system to operate a commercial wheelchair-mounted robotic manipulator (WMRM). In addition, a gesture recognition interface system was developed specially for individuals with upper-level spinal cord injuries including object tracking and face recognition to function as an efficient, hands-free WMRM controller. Design/methodology/approach – Two Kinect® cameras were used synergistically to perform a variety of simple object retrieval tasks. One camera was used to interpret the hand gestures and locate the oper

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P, Kaythry, Jegadish Kumar K.J, Vinu R, Balaji M, and Errshaad Ahamed M. "Hand Cricket Game Application Using Computer Vision." JOURNAL OF HIGH-FREQUENCY COMMUNICATION TECHNOLOGIES 01, no. 04 (2023): 111–19. http://dx.doi.org/10.58399/pspd6084.

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Hand cricket is an attempt to experience the childhood entrainment of playing cricket using our hands. This paper presents an application built to play Hand cricket using a computer vision-based real-time 3D hand gesture recognition system. To play the game the user will give inputs using hand gestures. These hand gestures will be identified using a real-time computer vision system that runs based on a CNN model. Since in this game both players must play simultaneously the computer also comes up with a number from 1 to 6 when a hand gesture is being read and recognized. The core engine of buil

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Kaur, Dr Mandeep. "IndiSign: A Universe of Hand Gesture and Meaning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46643.

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Abstract IndiSign Universe of Hand Gesture and Meaning Interpreter is a machine learning-based assistive communication system that seeks to bridge the communicative gap between People who talk or listen with speech or hearing disabilities. Unlike traditional pre-configured gesture-based sign language interpreters, IndiSign allows users to create and train a customized sign vocabulary. Personalized gestures are detected by real-time computer vision and machine learning, and are immediately translated to text or synthesized speech. The web-based system uses standard webcams with light models lik

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M, Monojit Bhattacharya. "CNN-Based Hand Gesture Recognition System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34543.

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Sign language is a system of communication using Visual gestures and signs.Hearing impaired people and the deaf and dumb community use sign language as their only means of communication. Understanding sign language is so much difficult for a normal person. Therefore, the minority group has always faced many difficulties in communicating with the General population. In this research paper, we proposed a new deep learning-based approach to detect sign language, which canremove the barrier of communication between normal and deaf People. To detect real-time sign language first we prepared a Datas

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Ewe, Edmond Li Ren, Chin Poo Lee, Lee Chung Kwek, and Kian Ming Lim. "Hand Gesture Recognition via Lightweight VGG16 and Ensemble Classifier." Applied Sciences 12, no. 15 (2022): 7643. http://dx.doi.org/10.3390/app12157643.

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Gesture recognition has been studied for a while within the fields of computer vision and pattern recognition. A gesture can be defined as a meaningful physical movement of the fingers, hands, arms, or other parts of the body with the purpose to convey information for the environment interaction. For instance, hand gesture recognition (HGR) can be used to recognize sign language which is the primary means of communication by the deaf and mute. Vision-based HGR is critical in its application; however, there are challenges that will need to be overcome such as variations in the background, illum

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Varma, Anshal, Sanyukta Pawaskar, Sumedh More, and Ashwini Raorane. "Computer Control Using Vision-Based Hand Motion Recognition System." ITM Web of Conferences 44 (2022): 03069. http://dx.doi.org/10.1051/itmconf/20224403069.

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In our day-to-day communication and expression, gestures play a crucial role. As a result, using them to interact with technical equipment requires small cognitive data processing on our part. Because it creates a large barrier between the user and the machine, using a physical device for human-computer interaction, such as a mouse or keyboard, obstructs the natural interface. In this study, we created a sophisticated marker-free hand gesture detection structure that can monitor both dynamic and static hand gestures. Our system turns motion detection into actions such as opening web pages and

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Kumar, C. Sathish, S. Silvia Priscila, G. Abishabackiyavathi, S. Suman Rajest, R. Regin, and Chunhua Deming. "Deciphering Hand Movements in Individuals with Limited Mobility Using Neural Networks." FMDB Transactions on Sustainable Computing Systems 2, no. 1 (2024): 13–21. http://dx.doi.org/10.69888/ftscs.2024.000193.

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The identification and detection of hand motions is the focus of this project. Using a web camera, hand gesture photographs are captured. These images are then compared to database images, with the best match returned. In order to create user-friendly interfaces, gesture recognition is one of the most important strategies. For instance, a robot that can identify hand gestures can accept commands from people. Similarly, a robot that can understand sign language would enable people who are deaf or hard of hearing to communicate with it. Recognition of hand gestures may make it possible to use a

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Ge, Chang, and Jianhua Min. "Hand gesture recognition in natural human-computer interaction." Applied and Computational Engineering 36, no. 1 (2024): 111–18. http://dx.doi.org/10.54254/2755-2721/36/20230430.

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This paper introduces the definition of Gesture recognition. First the article gives a precise definition of gesture recognition and explains the difference between gestures and postures, then it reveals technical difficulties of Gesture recognition. These technical difficulties include four aspects. After analyzing the technology and methods of Gesture recognition and the technical difficulties, it concretely expounds gesture recognition process based on data gloves, which is widely studied before and it also introduces the computer vision based research achievement which is currently becomin

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Shukla, Akhilesh, Dr Devesh Katiyar, and Mr Gaurav Goel. "Gesture Recognition-based AI Virtual Mouse." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (2022): 1583–88. http://dx.doi.org/10.22214/ijraset.2022.40937.

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Abstract: An amazing invention in Computer Technology is the mouse. Nowadays, A Bluetooth mouse or wireless mouse still has some limitations as it requires a battery for power and a dongle to connect to a PC. This issue may be solved in the proposed gesture-based AI virtual mouse by capturing hand motions and revealing hand tips with a webcam or integrated camera, as gestures are a powerful means of communication between people. Based on hand gestures, the computer can be almost controlled and can perform right- clicking, left-clicking, scrolling and computer cursor functions without using the

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Prof., C. D. Sawarkar Vivek Vaidya Vansh Sharma Samir Sheikh Aniket Neware Prathmesh Chaudhari. "AI Based Real Time Hand Gesture Recognition System." International Journal of Advanced Innovative Technology in Engineering 9, no. 3 (2024): 320–23. https://doi.org/10.5281/zenodo.12747525.

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This research presents a comprehensive approach for real-time hand gesture recognition using a synergistic combination of TensorFlow, OpenCV, and Media Pipe. Hand gesture recognition holds immense potential for natural and intuitive human-computer interaction in various applications, such as augmented reality, virtual reality, and human computer interfaces. The proposed system leverages the strengths of TensorFlow for deep learning-based model development, OpenCV for computer vision tasks, and Media Pipe for efficient hand landmark detection. The workflow begins with hand detection using OpenC

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Singh, Shaminder, Anuj Kumar Gupta, and Tejwant Singh. "Computer Vision based Hand Gesture Recognition A Survey." International Journal of Computer Sciences and Engineering 7, no. 5 (2019): 507–15. http://dx.doi.org/10.26438/ijcse/v7i5.507515.

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Badi, Haitham. "Recent methods in vision-based hand gesture recognition." International Journal of Data Science and Analytics 1, no. 2 (2016): 77–87. http://dx.doi.org/10.1007/s41060-016-0008-z.

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Pati, Rishav Nath, Nagalakshmi Vallabhaneni, Prabhavathy P, Yash Shekhawat, and Srijan Paria. "Real-Time Computer Vision Based Hand Gesture Recognition." International Research Journal on Advanced Science Hub 5, Issue 05S (2023): 382–91. http://dx.doi.org/10.47392/irjash.2023.s052.

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Mishra, Ashutosh, Jinhyuk Kim, Jaekwang Cha, Dohyun Kim, and Shiho Kim. "Authorized Traffic Controller Hand Gesture Recognition for Situation-Aware Autonomous Driving." Sensors 21, no. 23 (2021): 7914. http://dx.doi.org/10.3390/s21237914.

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An authorized traffic controller (ATC) has the highest priority for direct road traffic. In some irregular situations, the ATC supersedes other traffic control. Human drivers indigenously understand such situations and tend to follow the ATC; however, an autonomous vehicle (AV) can become confused in such circumstances. Therefore, autonomous driving (AD) crucially requires a human-level understanding of situation-aware traffic gesture recognition. In AVs, vision-based recognition is particularly desirable because of its suitability; however, such recognition systems have various bottlenecks, s

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