Mexican sign language segmentation using color based neuronal networks to detect the individual skin color

Espejel-Cabrera, Josué; Cervantes, Jair; García-Lamont, Farid; Castilla, José Sergio Ruiz; Jalili, Laura D.

doi:10.1016/j.eswa.2021.115295

Cited by 15 publications

(10 citation statements)

References 33 publications

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“…Cabrera et al 25 used neural networks to detect skin colour and then extract features from their 2241 keyframes extracted from 249 videos. Tamiru et al 12 extracted 34 shape, motion and colour features to obtain their high classification accuracy.…”

Section: Resultsmentioning

confidence: 99%

“…Most of these studies extract specific features and then use machine learning algorithms to classify the SL images. Many different SL have been used in these studies, namely American 3 – 11 , Amharic SL 12 , Arabic SL 13 – 17 , British SL 18 , 19 , Chinese SL 20 , 21 , German SL 22 , 23 , Indian SL 24 , Mexican SL 25 , Pakistani SL 26 – 31 , Persian SL 32 , and more in combination such as American and German SL 33 , American and Thai SL 34 and American and Indian SL 35 .…”

Section: Literature Reviewmentioning

confidence: 99%

“…Athira et al, used Indian SL with Zernike moments and centroid of signs to recognize static signs with 90.1% and dynamic signs with 89% accuracies using SVM 24 . Cabrera et al, obtained a 96.27% accuracy by classifying dynamic Mexican SL using SVM and Geometric features, such as Fourier descriptors, Hu moments, Ellipse, Gupta descriptors and Flusser moments 25 . Joshi et al, used static American and Indian SL, using shape-based features and using SVM obtained accuracies of 98.6% using Indian SL with uniform background, and 98.8% using Jochen–Triesch static hand posture with uniform background datasets 35 .…”

Section: Literature Reviewmentioning

confidence: 99%

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Vision-based Pakistani sign language recognition using bag-of-words and support vector machines

Mirza

Munaf

Azim

et al. 2022

Sci Rep

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In order to perform their daily activities, a person is required to communicating with others. This can be a major obstacle for the deaf population of the world, who communicate using sign languages (SL). Pakistani Sign Language (PSL) is used by more than 250,000 deaf Pakistanis. Developing a SL recognition system would greatly facilitate these people. This study aimed to collect data of static and dynamic PSL alphabets and to develop a vision-based system for their recognition using Bag-of-Words (BoW) and Support Vector Machine (SVM) techniques. A total of 5120 images for 36 static PSL alphabet signs and 353 videos with 45,224 frames for 3 dynamic PSL alphabet signs were collected from 10 native signers of PSL. The developed system used the collected data as input, resized the data to various scales and converted the RGB images into grayscale. The resized grayscale images were segmented using Thresholding technique and features were extracted using Speeded Up Robust Feature (SURF). The obtained SURF descriptors were clustered using K-means clustering. A BoW was obtained by computing the Euclidean distance between the SURF descriptors and the clustered data. The codebooks were divided into training and testing using fivefold cross validation. The highest overall classification accuracy for static PSL signs was 97.80% at 750 × 750 image dimensions and 500 Bags. For dynamic PSL signs a 96.53% accuracy was obtained at 480 × 270 video resolution and 200 Bags.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Vision-based Pakistani sign language recognition using bag-of-words and support vector machines

Mirza

Munaf

Azim

et al. 2022

Sci Rep

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“…Espejel-Cabrera et al developed a method for the recognition of 249 dynamic words of MSL [19]. Eleven people participated in generating a dataset of 2480 videos, each containing a dynamic word.…”

Section: Related Workmentioning

confidence: 99%

“…To analyze the classification performance of the model in each experiment, six metrics were used as defined in Equations ( 15)- (19), with the area under the curve (AUC) for the received operating characteristics (ROC) curve [37].…”

Section: Training and Classification Using Only Cartesian Features (1...mentioning

confidence: 99%

Use of Spherical and Cartesian Features for Learning and Recognition of the Static Mexican Sign Language Alphabet

et al. 2022

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The automatic recognition of sign language is very important to allow for communication by hearing impaired people. The purpose of this study is to develop a method of recognizing the static Mexican Sign Language (MSL) alphabet. In contrast to other MSL recognition methods, which require a controlled background and permit changes only in 2D space, our method only requires indoor conditions and allows for variations in the 3D pose. We present an innovative method that can learn the shape of each of the 21 letters from examples. Before learning, each example in the training set is normalized in the 3D pose using principal component analysis. The input data are created with a 3D sensor. Our method generates three types of features to represent each shape. When applied to a dataset acquired in our laboratory, an accuracy of 100% was obtained. The features used by our method have a clear, intuitive geometric interpretation.

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A New Selection and Class Prediction Using Type-1 Fuzzy Logic Applied to a Convolutional Neural Network

Poma

Melín

2023

Studies in Computational Intelligence

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Mexican sign language segmentation using color based neuronal networks to detect the individual skin color

Cited by 15 publications

References 33 publications

Vision-based Pakistani sign language recognition using bag-of-words and support vector machines

Vision-based Pakistani sign language recognition using bag-of-words and support vector machines

Use of Spherical and Cartesian Features for Learning and Recognition of the Static Mexican Sign Language Alphabet

A New Selection and Class Prediction Using Type-1 Fuzzy Logic Applied to a Convolutional Neural Network

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