Hand posture recognition in a body-face centered space

Marcel, Sébastien

doi:10.1145/632716.632901

Cited by 47 publications

(10 citation statements)

References 6 publications

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“…The Sebastian Marcel Static Hand Gesture Dataset [70] was used to train a neural network model to detect hand positions in photographs. Space discretization was used to separate hand movements based on facial position and body anthropometry.…”

Section: Sebastian Marcel Static Hand Gesture Datasetmentioning

confidence: 99%

“…The dataset has ten persons demonstrating six hand postures (a, b, c, point, five, and v) in uniform and complicated backdrops with different picture sizes based on the hand gesture. Figure 14 [70] depicts several photos from this collection. The dataset is divided into three folders: "Hand Postures" (2000 images), "Hand Postures with human noise" (750 images), and "Backgrounds" (2000 images).…”

Section: Sebastian Marcel Static Hand Gesture Datasetmentioning

confidence: 99%

“…The MU HandImages A.S.L. dataset is a collection of 26 unique hand gestures that are frequently employed in American Sign Language (ASL).Sebastian Marcel Static Hand Gesture DatasetThe Sebastian Marcel Static Hand Gesture Dataset[70] was used to train a neural network model to detect hand positions in photographs. Space discretization was used to separate hand movements based on facial position and body anthropometry.…”

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confidence: 99%

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confidence: 99%

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A Hybrid Image Augmentation Technique for User- and Environment-Independent Hand Gesture Recognition Based on Deep Learning

Awaluddin,

Chao,

Chiou

2024

Mathematics

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This research stems from the increasing use of hand gestures in various applications, such as sign language recognition to electronic device control. The focus is the importance of accuracy and robustness in recognizing hand gestures to avoid misinterpretation and instruction errors. However, many experiments on hand gesture recognition are conducted in limited laboratory environments, which do not fully reflect the everyday use of hand gestures. Therefore, the importance of an ideal background in hand gesture recognition, involving only the signer without any distracting background, is highlighted. In the real world, the use of hand gestures involves various unique environmental conditions, including differences in background colors, varying lighting conditions, and different hand gesture positions. However, the datasets available to train hand gesture recognition models often lack sufficient variability, thereby hindering the development of accurate and adaptable systems. This research aims to develop a robust hand gesture recognition model capable of operating effectively in diverse real-world environments. By leveraging deep learning-based image augmentation techniques, the study seeks to enhance the accuracy of hand gesture recognition by simulating various environmental conditions. Through data duplication and augmentation methods, including background, geometric, and lighting adjustments, the diversity of the primary dataset is expanded to improve the effectiveness of model training. It is important to note that the utilization of the green screen technique, combined with geometric and lighting augmentation, significantly contributes to the model’s ability to recognize hand gestures accurately. The research results show a significant improvement in accuracy, especially with implementing the proposed green screen technique, underscoring its effectiveness in adapting to various environmental contexts. Additionally, the study emphasizes the importance of adjusting augmentation techniques to the dataset’s characteristics for optimal performance. These findings provide valuable insights into the practical application of hand gesture recognition technology and pave the way for further research in tailoring techniques to datasets with varying complexities and environmental variations.

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Section: Sebastian Marcel Static Hand Gesture Datasetmentioning

confidence: 99%

Section: Sebastian Marcel Static Hand Gesture Datasetmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Hybrid Image Augmentation Technique for User- and Environment-Independent Hand Gesture Recognition Based on Deep Learning

Awaluddin,

Chao,

Chiou

2024

Mathematics

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“…We also compare our accuracy on the Marcel dataset to the one in refs. [24,25], as they also both use different state-of-the-art methods on the same dataset. The accuracy of our proposed method has improved from the one in ref.…”

Section: Introductionmentioning

confidence: 99%

Visual Static Hand Gesture Recognition Using Convolutional Neural Network

Eid

Schwenker

2023

Algorithms

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Hand gestures are an essential part of human-to-human communication and interaction and, therefore, of technical applications. The aim is increasingly to achieve interaction between humans and computers that is as natural as possible, for example, by means of natural language or hand gestures. In the context of human-machine interaction research, these methods are consequently being explored more and more. However, the realization of natural communication between humans and computers is a major challenge. In the field of hand gesture recognition, research approaches are being pursued that use additional hardware, such as special gloves, to classify gestures with high accuracy. Recently, deep learning techniques using artificial neural networks have been increasingly proposed for the problem of gesture recognition without using such tools. In this context, we explore the approach of convolutional neural network (CNN) in detail for the task of hand gesture recognition. CNN is a deep neural network that can be used in the fields of visual object processing and classification. The goal of this work is to recognize ten types of static hand gestures in front of complex backgrounds and different hand sizes based on raw images without the use of extra hardware. We achieved good results with a CNN network architecture consisting of seven layers. Through data augmentation and skin segmentation, a significant increase in the model’s accuracy was achieved. On public benchmarks, two challenging datasets have been classified almost perfectly, with testing accuracies of 96.5% and 96.57%.

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Gesture Recognition Performance Score: A New Metric to Evaluate Gesture Recognition Systems

Pisharady

Saerbeck

2015

Computer Vision - ACCV 2014 Workshops

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Hand posture recognition in a body-face centered space

Cited by 47 publications

References 6 publications

A Hybrid Image Augmentation Technique for User- and Environment-Independent Hand Gesture Recognition Based on Deep Learning

A Hybrid Image Augmentation Technique for User- and Environment-Independent Hand Gesture Recognition Based on Deep Learning

Visual Static Hand Gesture Recognition Using Convolutional Neural Network

Gesture Recognition Performance Score: A New Metric to Evaluate Gesture Recognition Systems

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