Real-Time Hand Detection using Convolutional Neural Networks for Costa Rican Sign Language Recognition

Zamora-Mora, Juan; Chacón-Rivas, Mario

doi:10.1109/contie49246.2019.00042

Cited by 11 publications

(4 citation statements)

References 21 publications

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“…With the development of deep learning, SLR gradually relies on neural networks [19]. Zamora-Mora [33] used CNN for real-time hand detection as the tool of SLR. Al-Hammadi [34] used 3DCNN to extract temporal and spatial features simultaneously and got better recognition performance.…”

Section: Network Learningmentioning

confidence: 99%

Attention-Based Sign Language Recognition Network Utilizing Keyframe Sampling and Skeletal Features

Pan

Zhang

2020

IEEE Access

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Sign language recognition(SLR) is a multidisciplinary research topic in pattern recognition and computer vision. Due to large amount of data from the continuous frames of sign language videos, selecting representative data to eliminate irrelevant information has always been a challenging problem in data preprocessing of sign language samples. In recent years, skeletal data emerged as a new type of data but received insufficient attention. Meanwhile, due to the increasing diversity of sign language features, making full use of them has also been an important research topic. In this paper, we improve keyframecentered clips(KCC) sampling to get a new kind of sampling method called optimized keyframe-centered clips(OptimKCC) sampling to select key actions from sign language videos. Besides, we design a new kind of skeletal feature called Multi-Plane Vector Relation(MPVR) to describe the video samples. Finally, combined with the attention mechanism, we also use Attention-Based networks to distribute weights to the temporal features and the spatial features extracted from skeletal data. We implement comparison experiments on our own and the public sign language dataset under the Signer-Independent and the Signer-Dependent circumstances to show the advantages of our methods.

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Section: Network Learningmentioning

confidence: 99%

Attention-Based Sign Language Recognition Network Utilizing Keyframe Sampling and Skeletal Features

Pan

Zhang

2020

IEEE Access

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“…We strongly suggest that their contexts be predefined, as in our case and that, by following the steps presented in this paper, they identify those contexts with lower similitude as candidates for a first proof of concept or a prototype. The specific techniques used for automatic recognition are not the focus of this paper, but the use of deep learning, for example convolutional neural networks, has been strongly suggested [47]. For instance, for our eight contexts, prioritization would be based on the data shown in Table 7.…”

Section: Clustering Generated By K-meansmentioning

confidence: 99%

Phonological Proximity in Costa Rican Sign Language

et al. 2020

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The study of phonological proximity makes it possible to establish a basis for future decision-making in the treatment of sign languages. Knowing how close a set of signs are allows the interested party to decide more easily its study by clustering, as well as the teaching of the language to third parties based on similarities. In addition, it lays the foundation for strengthening disambiguation modules in automatic recognition systems. To the best of our knowledge, this is the first study of its kind for Costa Rican Sign Language (LESCO, for its Spanish acronym), and forms the basis for one of the modules of the already operational system of sign and speech editing called the International Platform for Sign Language Edition (PIELS). A database of 2665 signs, grouped into eight contexts, is used, and a comparison of similarity measures is made, using standard statistical formulas to measure their degree of correlation. This corpus will be especially useful in machine learning approaches. In this work, we have proposed an analysis of different similarity measures between signs in order to find out the phonological proximity between them. After analyzing the results obtained, we can conclude that LESCO is a sign language with high levels of phonological proximity, particularly in the orientation and location components, but they are noticeably lower in the form component. We have also concluded as an outstanding contribution of our research that automatic recognition systems can take as a basis for their first prototypes the contexts or sign domains that map to clusters with lower levels of similarity. As mentioned, the results obtained have multiple applications such as in the teaching area or the Natural Language Processing area for automatic recognition tasks.

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“…Hand gestures are one of the most natural ways of interaction between people (e.g. sign language [1][2][3][4][5][6]24]) or Human Computer Interaction-HCI [7][8][9]. Conveying messages in sign language through hand gestures has attracted the attention of many researchers and technology developers.…”

Section: Introductionmentioning

confidence: 99%

Fusion Machine Learning Strategies for Multi-modal Sensor-based Hand Gesture Recognition

Doan

Nguyen

2022

Eng. Technol. Appl. Sci. Res.

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Hand gesture recognition has attracted the attention of many scientists, because of its high applicability in fields such as sign language expression and human machine interaction. Many approaches have been deployed to detect and recognize hand gestures, like wearable devices, image information, and/or a combination of sensors and computer vision. However, the method of using wearable sensors brings much higher accuracy and is less affected by occlusion, lighting conditions, and complex background. Existing solutions separately utilize sensor information and/or only use sensor information processing and decision-making algorithms over conventional threshold comparison algorithms and do not analyze data or utilize machine learning algorithms. In this paper, a multi-modal solution is proposed that combines information for measuring the curvature of the fingers and sensors for measuring angular velocity and acceleration. The provided information from the sensors is normalized and analyzed and various fusion strategies are used. Then, the most suitable algorithm for these sensor-based multiple modalities is proposed. The proposed system also analyzes the differences between gestures and actions that are almost similar but in fact, they are just normal moving gestures.

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Real-Time Hand Detection using Convolutional Neural Networks for Costa Rican Sign Language Recognition

Cited by 11 publications

References 21 publications

Attention-Based Sign Language Recognition Network Utilizing Keyframe Sampling and Skeletal Features

Attention-Based Sign Language Recognition Network Utilizing Keyframe Sampling and Skeletal Features

Phonological Proximity in Costa Rican Sign Language

Fusion Machine Learning Strategies for Multi-modal Sensor-based Hand Gesture Recognition

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