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Search Results (11)
  • Open Access


    Virtual Keyboard: A Real-Time Hand Gesture Recognition-Based Character Input System Using LSTM and Mediapipe Holistic

    Bijon Mallik1, Md Abdur Rahim1, Abu Saleh Musa Miah2, Keun Soo Yun3,*, Jungpil Shin2

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 555-570, 2024, DOI:10.32604/csse.2023.045981

    Abstract In the digital age, non-touch communication technologies are reshaping human-device interactions and raising security concerns. A major challenge in current technology is the misinterpretation of gestures by sensors and cameras, often caused by environmental factors. This issue has spurred the need for advanced data processing methods to achieve more accurate gesture recognition and predictions. Our study presents a novel virtual keyboard allowing character input via distinct hand gestures, focusing on two key aspects: hand gesture recognition and character input mechanisms. We developed a novel model with LSTM and fully connected layers for enhanced sequential data… More >

  • Open Access


    Japanese Sign Language Recognition by Combining Joint Skeleton-Based Handcrafted and Pixel-Based Deep Learning Features with Machine Learning Classification

    Jungpil Shin1,*, Md. Al Mehedi Hasan2, Abu Saleh Musa Miah1, Kota Suzuki1, Koki Hirooka1

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 2605-2625, 2024, DOI:10.32604/cmes.2023.046334

    Abstract Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing communities. In Japan, approximately 360,000 individuals with hearing and speech disabilities rely on Japanese Sign Language (JSL) for communication. However, existing JSL recognition systems have faced significant performance limitations due to inherent complexities. In response to these challenges, we present a novel JSL recognition system that employs a strategic fusion approach, combining joint skeleton-based handcrafted features and pixel-based deep learning features. Our system incorporates two distinct streams: the first stream extracts crucial handcrafted features, emphasizing the capture of hand and body… More >

  • Open Access


    Deep Learning Approach for Hand Gesture Recognition: Applications in Deaf Communication and Healthcare

    Khursheed Aurangzeb1, Khalid Javeed2, Musaed Alhussein1, Imad Rida3, Syed Irtaza Haider1, Anubha Parashar4,*

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 127-144, 2024, DOI:10.32604/cmc.2023.042886

    Abstract Hand gestures have been used as a significant mode of communication since the advent of human civilization. By facilitating human-computer interaction (HCI), hand gesture recognition (HGRoc) technology is crucial for seamless and error-free HCI. HGRoc technology is pivotal in healthcare and communication for the deaf community. Despite significant advancements in computer vision-based gesture recognition for language understanding, two considerable challenges persist in this field: (a) limited and common gestures are considered, (b) processing multiple channels of information across a network takes huge computational time during discriminative feature extraction. Therefore, a novel hand vision-based convolutional neural network… More >

  • Open Access


    Appearance Based Dynamic Hand Gesture Recognition Using 3D Separable Convolutional Neural Network

    Muhammad Rizwan1,*, Sana Ul Haq1,*, Noor Gul1,2, Muhammad Asif1, Syed Muslim Shah3, Tariqullah Jan4, Naveed Ahmad5

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1213-1247, 2023, DOI:10.32604/cmc.2023.038211

    Abstract Appearance-based dynamic Hand Gesture Recognition (HGR) remains a prominent area of research in Human-Computer Interaction (HCI). Numerous environmental and computational constraints limit its real-time deployment. In addition, the performance of a model decreases as the subject’s distance from the camera increases. This study proposes a 3D separable Convolutional Neural Network (CNN), considering the model’s computational complexity and recognition accuracy. The 20BN-Jester dataset was used to train the model for six gesture classes. After achieving the best offline recognition accuracy of 94.39%, the model was deployed in real-time while considering the subject’s attention, the instant of… More >

  • Open Access


    An Efficient and Robust Hand Gesture Recognition System of Sign Language Employing Finetuned Inception-V3 and Efficientnet-B0 Network

    Adnan Hussain1, Sareer Ul Amin2, Muhammad Fayaz3, Sanghyun Seo4,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3509-3525, 2023, DOI:10.32604/csse.2023.037258

    Abstract Hand Gesture Recognition (HGR) is a promising research area with an extensive range of applications, such as surgery, video game techniques, and sign language translation, where sign language is a complicated structured form of hand gestures. The fundamental building blocks of structured expressions in sign language are the arrangement of the fingers, the orientation of the hand, and the hand’s position concerning the body. The importance of HGR has increased due to the increasing number of touchless applications and the rapid growth of the hearing-impaired population. Therefore, real-time HGR is one of the most effective… More >

  • Open Access


    Hand Gesture Recognition for Disabled People Using Bayesian Optimization with Transfer Learning

    Fadwa Alrowais1, Radwa Marzouk2,3, Fahd N. Al-Wesabi4,*, Anwer Mustafa Hilal5

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3325-3342, 2023, DOI:10.32604/iasc.2023.036354

    Abstract Sign language recognition can be treated as one of the efficient solutions for disabled people to communicate with others. It helps them to convey the required data by the use of sign language with no issues. The latest developments in computer vision and image processing techniques can be accurately utilized for the sign recognition process by disabled people. American Sign Language (ASL) detection was challenging because of the enhancing intraclass similarity and higher complexity. This article develops a new Bayesian Optimization with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication (BODL-HGRSLC) for Disabled People.… More >

  • Open Access


    A Novel Machine Learning–Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

    Saurabh Adhikari1, Tushar Kanti Gangopadhayay1, Souvik Pal2,3, D. Akila4, Mamoona Humayun5, Majed Alfayad6, N. Z. Jhanjhi7,*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2123-2140, 2023, DOI:10.32604/csse.2023.034431

    Abstract Machine learning is a technique for analyzing data that aids the construction of mathematical models. Because of the growth of the Internet of Things (IoT) and wearable sensor devices, gesture interfaces are becoming a more natural and expedient human-machine interaction method. This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns. The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition. Potential applications of hand gesture recognition research… More >

  • Open Access


    A Novel SE-CNN Attention Architecture for sEMG-Based Hand Gesture Recognition

    Zhengyuan Xu1,2,#, Junxiao Yu1,#, Wentao Xiang1, Songsheng Zhu1, Mubashir Hussain3, Bin Liu1,*, Jianqing Li1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 157-177, 2023, DOI:10.32604/cmes.2022.020035

    Abstract In this article, to reduce the complexity and improve the generalization ability of current gesture recognition systems, we propose a novel SE-CNN attention architecture for sEMG-based hand gesture recognition. The proposed algorithm introduces a temporal squeeze-and-excite block into a simple CNN architecture and then utilizes it to recalibrate the weights of the feature outputs from the convolutional layer. By enhancing important features while suppressing useless ones, the model realizes gesture recognition efficiently. The last procedure of the proposed algorithm is utilizing a simple attention mechanism to enhance the learned representations of sEMG signals to perform More >

  • Open Access


    Intelligent Sign Language Recognition System for E-Learning Context

    Muhammad Jamil Hussain1, Ahmad Shaoor1, Suliman A. Alsuhibany2, Yazeed Yasin Ghadi3, Tamara al Shloul4, Ahmad Jalal1, Jeongmin Park5,*

    CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 5327-5343, 2022, DOI:10.32604/cmc.2022.025953

    Abstract In this research work, an efficient sign language recognition tool for e-learning has been proposed with a new type of feature set based on angle and lines. This feature set has the ability to increase the overall performance of machine learning algorithms in an efficient way. The hand gesture recognition based on these features has been implemented for usage in real-time. The feature set used hand landmarks, which were generated using media-pipe (MediaPipe) and open computer vision (openCV) on each frame of the incoming video. The overall algorithm has been tested on two well-known ASL-alphabet More >

  • Open Access


    Robust Interactive Method for Hand Gestures Recognition Using Machine Learning

    Amal Abdullah Mohammed Alteaimi1,*, Mohamed Tahar Ben Othman1,2

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 577-595, 2022, DOI:10.32604/cmc.2022.023591

    Abstract The Hand Gestures Recognition (HGR) System can be employed to facilitate communication between humans and computers instead of using special input and output devices. These devices may complicate communication with computers especially for people with disabilities. Hand gestures can be defined as a natural human-to-human communication method, which also can be used in human-computer interaction. Many researchers developed various techniques and methods that aimed to understand and recognize specific hand gestures by employing one or two machine learning algorithms with a reasonable accuracy. This work aims to develop a powerful hand gesture recognition model with… More >

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