A real-time gesture recognition system using near-infrared imagery

Mantecón, Tomás; del-Blanco, Carlos R.; Jaureguizar, Fernando; García, Narciso

doi:10.1371/journal.pone.0223320

Cited by 26 publications

(19 citation statements)

References 23 publications

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“…To show the effectiveness our proposed model, we have compared our model with other existing schemes shown by [4] and [23] tabulated in Tables 7 and 8. It is observed in Table 7, that our proposed work has achieved a better gesture recognition rate (in terms of accuracy results) than the technique proposed by Mantecón T [4] over Dataset-1.…”

Section: Comparison With Other Schemesmentioning

confidence: 99%

“…Two publicly available gesture image datasets, hand gesture dataset[4] (labeled as Dataset-1), multi-modal hand gesture dataset[23] (labeled as Dataset-2), and one self-constructed dataset have been used to evaluate our proposed model. Dataset-1 contains 20,000 images of 10 distinct gestures (Palm, L, Fist, Fist move, Thumb, Index, Ok, Palm move, Close, and Palm down) performed by ten different people (five men and five women).…”

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confidence: 99%

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A Novel Hand Gesture Detection and Recognition system based on ensemble-based Convolutional Neural Network

Sen¹,

Mishra²,

Dash³

2022

Preprint

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Nowadays, hand gesture recognition has become an alternative for human-machine interaction. It has covered a large area of applications like 3D game technology, sign language interpreting, VR (virtual reality) environment, and robotics. But detection of the hand portion has become a challenging task in computer vision and pattern recognition communities. Deep learning algorithm like convolutional neural network (CNN) architecture has become a very popular choice for classification tasks, but CNN architectures suffer from some problems like high variance during prediction, overfitting problem and also prediction errors. To overcome these problems, an ensemble of CNN-based approaches is presented in this paper. Firstly, the gesture portion is detected by using the background separation method based on binary thresholding. After that, the contour portion is extracted, and the hand region is segmented. Then, the images have been resized and fed into three individual CNN models to train them in parallel. In the last part, the output scores of CNN models are averaged to construct an optimal ensemble model for the final prediction. Two publicly available datasets (labeled as Dataset-1 and Dataset-2) containing infrared images and one self-constructed dataset have been used to validate the proposed system. Exper-

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Section: Comparison With Other Schemesmentioning

confidence: 99%

mentioning

confidence: 99%

A Novel Hand Gesture Detection and Recognition system based on ensemble-based Convolutional Neural Network

Sen¹,

Mishra²,

Dash³

2022

Preprint

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“…An alternative to visible imagery is near-infrared (NIR) imagery, which is more robust for adverse illumination conditions, favouring its deployment. In Mantecón et al [12], a NIR-based hand gesture recognition system is proposed to interact within a complex immersive environment, which improves real-time performance of previous works, such as Mantecón et al [13]. Lastly, 3D or depth information is also used to improve the gesture localization, and thus the general performance.…”

Section: State-of-the-art In Gesture Recognition and Automated Sign Lmentioning

confidence: 99%

Is There Any Hope for Developing Automated Translation Technology for Sign Languages?

Jantunen¹,

Rousi²,

Rainò³

et al. 2021

Multilingual Facilitation

Self Cite

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This article discusses the prerequisites for the machine translation of sign languages. The topic is complex, including questions relating to technology, interaction design, linguistics and culture. At the moment, despite the affordances provided by the technology, automated translation between signed and spoken languages – or between sign languages – is not possible. The very need of such translation and its associated technology can also be questioned. Yet, we believe that contributing to the improvement of sign language detection, processing and even sign language translation to spoken languages in the future is a matter that should not be abandoned. However, we argue that this work should focus on all necessary aspects of sign languages and sign language user communities. Thus, a more diverse and critical perspective towards these issues is needed in order to avoid generalisations and bias that is often manifested within dominant research paradigms particularly in the fields of spoken language research and speech community.

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“…For polarity, the gradient of all the gesture coordinates was calculated using (17) and (18), and by (19), gestures with normal strokes were obtained. Then, to check the performance of global measure, using (20), the MER was calculated for 'ED', 'IV e ', and 'Po'. An MER of 0.0209, 0.0279, and 0.1818, respectively, was achieved when AT segments were matched with GT segments.…”

Section: Experiments 1: With a Reference Patternmentioning

confidence: 99%

Segregation of meaningful strokes, a pre‐requisite for self co‐articulation removal in isolated dynamic gestures

Monsley

Yadav

Misra

et al. 2020

IET Image Processing

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Gesture formation, a pre-processing step, has its importance when variations in patterns, scale, and speed come into play. Self co-articulations are intentional movements performed by an individual to complete a gesture, whose presence in the trajectory alters its original meaning. For recognition, most researchers have directly used the trajectory formed along with these self co-articulated strokes, with a few removing it using visible trait-like velocity. Usage of velocity has shortcomings as gesturing in air differs from gesturing over a solid surface; hence, we propose a gesture formation model, which incorporates global and local measures to remove these self co-articulations. The global measure uses Euclidean distance, instantaneous velocity, and polarity calculated from the complete gesture, while the local measure segments the gesture into stroke-level segments by using the minimummaximum-polarity algorithm and applies the selective bypass rules. The proposed model, when experimented on gestures patterns with premeditated speed variation, has a mean error rate of 0.0069 and 7.40% self co-articulations;individuals' natural gesticulation has a mean error rate of 0.0371 and 12.07% self co-articulations. Experimentation on each gesture of NITS hand gesture databases showed a relative improvement of 40% (accuracy 97%) over the existing baseline models.

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A real-time gesture recognition system using near-infrared imagery

Cited by 26 publications

References 23 publications

A Novel Hand Gesture Detection and Recognition system based on ensemble-based Convolutional Neural Network

A Novel Hand Gesture Detection and Recognition system based on ensemble-based Convolutional Neural Network

Is There Any Hope for Developing Automated Translation Technology for Sign Languages?

Segregation of meaningful strokes, a pre‐requisite for self co‐articulation removal in isolated dynamic gestures

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