A Deep Learning Approach for Hybrid Hand Gesture Recognition

Alonso, Diego Gabriel; Teyseyre, Alfredo Raúl; Berdún, Luis; Schiaffino, Silvia

doi:10.1007/978-3-030-33749-0_8

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…In this stage, the process of estimating an appropriate deep learning architecture is first performed. Pre-experiments uncovered that the hash model [43] results in better overall precision. For this reason, pre-trained architectures were combined inside a hash model.…”

Section: Overview Of Proposed Model For Hand Gesture Recognitionmentioning

confidence: 99%

A Novel Hyperparameter Optimization Aided Hand Gesture Recognition Framework Based on Deep Learning Algorithms

Yılmaz¹

2022

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The recognition of hand gestures in cluttered or complex environments is a vital research area in the human-computer interaction and computer vision fields due to its various potential applications, such as hand action analysis, driver hand behaviour monitoring, virtual reality, pose estimation, human action recognition, and sign language recognition. In order to create more reliable and efficient algorithms in this research field, various approaches have been suggested in recent years. However, a robust system is still elusive. For this reason, a new deep learning-based architecture for classifying hand gestures is suggested in this study; it is based on a hybrid model. The study makes two main contributions to the literature. The first is the creation of a new database for hand gesture recognition. The second is a novel hybrid architecture that combines two widely used pre-trained network models in an optimised manner, using a genetic algorithm for hyperparameter optimization. The proposed method comprises five main phases, namely, data acquisition, pre-processing, the design of the deep neural network architecture, hyperparameter optimization, the training of the proposed deep neural network architecture. The proposed method was tested on three comprehensive datasets. The experimental results reveal that the suggested method can effectively classify hand gestures with a high accuracy rate and that it outperforms the state-of-the-art methods in the literature.

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Section: Overview Of Proposed Model For Hand Gesture Recognitionmentioning

confidence: 99%

A Novel Hyperparameter Optimization Aided Hand Gesture Recognition Framework Based on Deep Learning Algorithms

Yılmaz¹

2022

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“…In [41], a deep-learning-based method for skeleton-based hand gesture recognition was described. The network architecture consists of a convolutional layer for extracting features and a long, short-term memory layer for modeling the temporal dimension.…”

Section: Related Workmentioning

confidence: 99%

Hand Posture Recognition Using Skeletal Data and Distance Descriptor

Kapuściński

Warchoł

2020

Applied Sciences

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In this paper, a method for the recognition of static hand postures based on skeletal data was presented. A novel descriptor was proposed. It encodes information about distances between particular hand points. Five different classifiers were tested, including four common methods and a proposed modification of nearest neighbor classifier, which can distinguish between posture classes differing mostly in hand orientation. The experiments were performed using three challenging datasets of gestures from Polish and American Sign Languages. The proposed method was compared with other approaches found in the literature. It outperforms every compared method, including our previous work, in terms of recognition rate.

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Exploring Hand Gesture Recognition Techniques for Enhanced Control of Bionic Hands

Yadav,

Patil

2024

2024 International Conference on Emerging Smart Computing and Informatics (ESCI)

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A Deep Learning Approach for Hybrid Hand Gesture Recognition

Cited by 4 publications

References 12 publications

A Novel Hyperparameter Optimization Aided Hand Gesture Recognition Framework Based on Deep Learning Algorithms

A Novel Hyperparameter Optimization Aided Hand Gesture Recognition Framework Based on Deep Learning Algorithms

Hand Posture Recognition Using Skeletal Data and Distance Descriptor

Exploring Hand Gesture Recognition Techniques for Enhanced Control of Bionic Hands

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