Shape geodesics for robust sign language recognition

Nasreddine, Kamal; Benzinou, Abdesslam

doi:10.1049/iet-ipr.2018.5282

Cited by 3 publications

(1 citation statement)

References 57 publications

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“…Kamal et al [29] proposed a pattern recognition method for static recognition, which is able to handle the low variability among the different gestures. Authors have used shape geodesics and robust registration for calculating the accelerated time.…”

Section: Literature Surveymentioning

confidence: 99%

Hand gesture classification using a novel CNN-crow search algorithm

Gadekallu

Alazab

Kaluri

et al. 2021

Complex Intell. Syst.

128

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Human–computer interaction (HCI) and related technologies focus on the implementation of interactive computational systems. The studies in HCI emphasize on system use, creation of new techniques that support user activities, access to information, and ensures seamless communication. The use of artificial intelligence and deep learning-based models has been extensive across various domains yielding state-of-the-art results. In the present study, a crow search-based convolution neural networks model has been implemented in gesture recognition pertaining to the HCI domain. The hand gesture dataset used in the study is a publicly available one, downloaded from Kaggle. In this work, a one-hot encoding technique is used to convert the categorical data values to binary form. This is followed by the implementation of a crow search algorithm (CSA) for selecting optimal hyper-parameters for training of dataset using the convolution neural networks. The irrelevant parameters are eliminated from consideration, which contributes towards enhancement of accuracy in classifying the hand gestures. The model generates 100 percent training and testing accuracy that justifies the superiority of the model against traditional state-of-the-art models.

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Section: Literature Surveymentioning

confidence: 99%