Robust Hand Gesture Recognition System Research Articles

HGR-FYOLO: a robust hand gesture recognition system for the normal and physically impaired person using frozen YOLOv5

HGR-FYOLO: a robust hand gesture recognition system for the normal and physically impaired person using frozen YOLOv5

Hand Gesture Recognition

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 algorithm. Traditionally, systems have used data gloves or markers as a means of input. We are free to use the system however we like. In this way, the user can make natural hand gestures in front of the camera. The system implemented serves as an extendable basis for future work toward a fully robust hand gesture recognition system, which is still the subject of intensive research and development.

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

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 interaction methods between computers and humans. Developing a user-free interface with good recognition performance should be the goal of real-time HGR systems. Nowadays, Convolutional Neural Network (CNN) shows great recognition rates for different image-level classification tasks. It is challenging to train deep CNN networks like VGG-16, VGG-19, Inception-v3, and Efficientnet-B0 from scratch because only some significant labeled image datasets are available for static hand gesture images. However, an efficient and robust hand gesture recognition system of sign language employing finetuned Inception-v3 and Efficientnet-Bo network is proposed to identify hand gestures using a comparative small HGR dataset. Experiments show that Inception-v3 achieved 90% accuracy and 0.93% precision, 0.91% recall, and 0.90% f1-score, respectively, while EfficientNet-B0 achieved 99% accuracy and 0.98%, 0.97%, 0.98%, precision, recall, and f1-score respectively.

Hand Gesture Detection and Recognition

The design introduces an operation using computer vision for Hand gesture recognition. A camera records a live videotape sluice, from which a shot is taken with the help of interface. The system is trained for each type of count hand gestures (one, two, three, four, and five) at least formerly. After that a test gesture is given to it and the system tries to fete it. A exploration was carried out on a number of algorithms that could best separate a hand gesture. It was set up that the slant sum algorithm gave the loftiest delicacy rate. In the preprocessing phase, a tone- developed algorithm removes the background of each training gesture. After that the image is converted into a double image and the totalities of all slant rudiments of the picture are taken. This sum helps us in secerning and classifying different hand gestures. former systems have used data gloves or labels for input in the system. I've no similar constraints for using the system. The stoner can give hand gestures in view of the camera naturally. A fully robust hand gesture recognition system is still under heavy exploration and development; the enforced system serves as an extendible foundation for unborn work.

Robust Hand Gestures Recognition Using a Deep CNN and Thermal Images

Medical systems and assistive technologies, human-computer interaction, human-robot interaction, industrial automation, virtual environment control, sign language translation, crisis and disaster management, entertainment and computer games, and so on all use RGB cameras for hand gesture recognition. However, their performance is limited especially in low-light conditions. In this paper, we propose a robust hand gesture recognition system based on high-resolution thermal imaging that is light-independent. A dataset of 14,400 thermal hand gestures is constructed, separated into two color tones. We also propose using a deep CNN to classify high-resolution hand gestures accurately. The proposed models were also tested on Raspberry Pi 4 and Nvidia AGX edge computing devices, and the results were compared to the benchmark models. The model also achieves an accuracy of 98.81% and an inference time of 75.138 ms on Nvidia Jetson AGX. In contrast to hand gesture recognition systems based on RGB cameras, which have limited performance in the dark-light conditions, the proposed system based on reliable high resolution thermal images is well-suited to a wide range of applications.

Robust hand gesture recognition system based on a new set of quaternion Tchebichef moment invariants

Hand gesture recognition is a challenging task due to the complexity of hand movements and to the variety among the same gesture performed by distinct subjects. Recent technologies, such as Kinect sensor, provide new opportunities, allowing to capture both RGB and depth (RGB-D) images, which offer high discriminant information for efficient hand gesture recognition. In the aspect of feature extraction, the traditional methods process the RGB and depth information independently. In this paper, we propose a robust hand gesture recognition system based on a new feature extraction method, fusing RGB images and depth information simultaneously, by using the quaternion algebra that provide a more robust and holistical representation. In fact, we introduce, for the first time, a novel type of feature extraction method, named quaternion Tchebichef moment invariants. The novelty of the proposed method in this paper lies in the direct derivation of invariants from their orthogonal moments, based on the algebraic properties of the discrete Tchebichef polynomials. The proposed approach based on quaternion algebra is suggested to process the four components holistically, for a robust and efficient hand gesture recognition system. The obtained experimental and theoretical results demonstrate that the present approach is very effective for addressing the problem of hand gesture recognition and have proved its robustness against geometrical distortion, noisy conditions and complex background compared to the state of the art, indicating that it could be highly useful for many computer vision applications.

Hand Gesture Recognition for Sign Language Using 3DCNN

Recently, automatic hand gesture recognition has gained increasing importance for two principal reasons: the growth of the deaf and hearing-impaired population, and the development of vision-based applications and touchless control on ubiquitous devices. As hand gesture recognition is at the core of sign language analysis a robust hand gesture recognition system should consider both spatial and temporal features. Unfortunately, finding discriminative spatiotemporal descriptors for a hand gesture sequence is not a trivial task. In this study, we proposed an efficient deep convolutional neural networks approach for hand gesture recognition. The proposed approach employed transfer learning to beat the scarcity of a large labeled hand gesture dataset. We evaluated it using three gesture datasets from color videos: 40, 23, and 10 classes were used from these datasets. The approach obtained recognition rates of 98.12%, 100%, and 76.67% on the three datasets, respectively for the signer-dependent mode. For the signer-independent mode, it obtained recognition rates of 84.38%, 34.9%, and 70% on the three datasets, respectively.