Xiaorui Zhang^1,2,3,*, Xianglong Zeng¹, Wei Sun^3,4, Yongjun Ren^1,2,3, Tong Xu⁵

Computer Systems Science and Engineering, Vol.46, No.1, pp. 671-686, 2023, DOI:10.32604/csse.2023.035119

Abstract Gesture recognition technology enables machines to read human gestures and has significant application prospects in the fields of human-computer interaction and sign language translation. Existing researches usually use convolutional neural networks to extract features directly from raw gesture data for gesture recognition, but the networks are affected by much interference information in the input data and thus fit to some unimportant features. In this paper, we proposed a novel method for encoding spatio-temporal information, which can enhance the key features required for gesture recognition, such as shape, structure, contour, position and hand motion of gestures, thereby improving the accuracy of… More >

Sameena Javaid^*, Safdar Rizvi

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 523-537, 2023, DOI:10.32604/cmc.2023.031924

Abstract Sign language fills the communication gap for people with hearing and speaking ailments. It includes both visual modalities, manual gestures consisting of movements of hands, and non-manual gestures incorporating body movements including head, facial expressions, eyes, shoulder shrugging, etc. Previously both gestures have been detected; identifying separately may have better accuracy, but much communicational information is lost. A proper sign language mechanism is needed to detect manual and non-manual gestures to convey the appropriate detailed message to others. Our novel proposed system contributes as Sign Language Action Transformer Network (SLATN), localizing hand, body, and facial gestures in video sequences. Here… More >

Zhengyuan Xu^1,2,#, Junxiao Yu^1,#, Wentao Xiang¹, Songsheng Zhu¹, Mubashir Hussain³, Bin Liu^1,*, Jianqing Li^1,*

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 multi-channel sEMG-based gesture recognition tasks.… More >

Muhammad Jamil Hussain¹, Ahmad Shaoor¹, Suliman A. Alsuhibany², Yazeed Yasin Ghadi³, Tamara al Shloul⁴, Ahmad Jalal¹, Jeongmin Park^5,*

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 (American Sign Language) and ISL-HS… More >

Amal Abdullah Mohammed Alteaimi^1,*, Mohamed Tahar Ben Othman^1,2

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 a 100% recognition rate. We… More >

Muneeb Ur Rehman¹, Fawad Ahmed¹, Muhammad Attique Khan², Usman Tariq³, Faisal Abdulaziz Alfouzan⁴, Nouf M. Alzahrani⁵, Jawad Ahmad^6,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4675-4690, 2022, DOI:10.32604/cmc.2022.019586

Abstract Recognition of dynamic hand gestures in real-time is a difficult task because the system can never know when or from where the gesture starts and ends in a video stream. Many researchers have been working on vision-based gesture recognition due to its various applications. This paper proposes a deep learning architecture based on the combination of a 3D Convolutional Neural Network (3D-CNN) and a Long Short-Term Memory (LSTM) network. The proposed architecture extracts spatial-temporal information from video sequences input while avoiding extensive computation. The 3D-CNN is used for the extraction of spectral and spatial features which are then given to… More >

Mohammed Ibrahim Ahmed Al-mashhadani¹, Theyazn H. H. Aldhyani^2,*, Mosleh Hmoud Al-Adhaileh³, Alwi M. Bamhdi⁴, Mohammed Y. Alzahrani⁵, Fawaz Waselallah Alsaade⁶, Hasan Alkahtani^1,6

Computer Systems Science and Engineering, Vol.38, No.1, pp. 25-37, 2021, DOI:10.32604/csse.2021.014992

Abstract Touch gesture recognition is an important aspect in human–robot interaction, as it makes such interaction effective and realistic. The novelty of this study is the development of a system that recognizes human–animal affective robot touch (HAART) using a deep learning algorithm. The proposed system was used for touch gesture recognition based on a dataset provided by the Recognition of the Touch Gestures Challenge 2015. The dataset was tested with numerous subjects performing different HAART gestures; each touch was performed on a robotic animal covered by a pressure sensor skin. A convolutional neural network algorithm is proposed to implement the touch… More >

Shazia Saqib¹, Allah Ditta², Muhammad Adnan Khan^1,*, Syed Asad Raza Kazmi³, Hani Alquhayz⁴

CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2061-2076, 2021, DOI:10.32604/cmc.2020.013905

Abstract Human activity detection and recognition is a challenging task. Video surveillance can benefit greatly by advances in Internet of Things (IoT) and cloud computing. Artificial intelligence IoT (AIoT) based devices form the basis of a smart city. The research presents Intelligent dynamic gesture recognition (IDGR) using a Convolutional neural network (CNN) empowered by edit distance for video recognition. The proposed system has been evaluated using AIoT enabled devices for static and dynamic gestures of Pakistani sign language (PSL). However, the proposed methodology can work efficiently for any type of video. The proposed research concludes that deep learning and convolutional neural… More >

Ching‐Han Chen^a, Ching‐Yi Chen^b, Nai‐Yuan Liu^a

Intelligent Automation & Soft Computing, Vol.25, No.2, pp. 375-384, 2019, DOI:10.31209/2019.100000099

Abstract This study introduces a dynamic gesture recognition system applicable in IPTV remote control. In this system, we developed a hardware accelerator for realtime moving object detection. It is able to detect the position of hand block in each frame at high speed. After acquiring the information of hand block, the system can capture the robust dynamic gesture feature with the moving trail of hand block in the continuous images, and input to FNN classifier for starting recognition process. The experimental results show that our method has a good recognition performance, and more applicable to real gesture-controlled human-computer interactive environment. More >