Mexican Sign‐Language Static‐Alphabet Recognition Using 3D Affine Invariants

Carmona-Arroyo, Guadalupe; Ríos-Figueroa, Homero Vladimir; Avendaño-Garrido, Martha Lorena

doi:10.1002/9781119786122.ch9

Cited by 8 publications

(6 citation statements)

References 20 publications

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“…The method of Carmona-Arroyo et al uses 3D points clouds as input [30] (Table 2). Their method which used 3D affine invariants is interesting to describe the 3D shape.…”

Section: Discussionmentioning

confidence: 99%

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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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“…The method of Carmona-Arroyo et al uses 3D points clouds as input [30] (Table 2). Their method which used 3D affine invariants is interesting to describe the 3D shape.…”

Section: Discussionmentioning

confidence: 99%

“…Carmona-Arroyo et al recognized 21 static MSL letters using six 3D affine moment invariants [30]. The dataset was generated by nine signers, who performed each sign once.…”

Section: Related Workmentioning

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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“…Existen numerosos trabajos en el reconocimiento de lenguas de señas extranjeras, e.g. reconocimiento de señas mediante aprendizaje profundo (Rao et al, 2018;Xia et al, 2022;Kothadiya et al, 2022); sin embargo, hay poca investigación específica de la LSM y la mayoría se ha centrado en el recono-cimiento estático de la LSM (Solís-V et al, 2014;Solís et al, 2015Carmona-Arroyo et al, 2021;Rios-Figueroa et al, 2022). En el reconocimiento de señas estáticas y dinámicas de la LSM se encuentra (Martinez-Seis et al, 2019).…”

Section: Reconocimiento De La Lsmunclassified

Hacia la construcción de una base de datos abierta de la LSM

Ordaz-Hernández,

Castillo-Gaytán,

Rodríguez-Recio

et al. 2023

ICBI

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Este trabajo reporta la fase inicial de la creación de una base de datos visual del alfabeto dactilológico de la LSM. El diseño de la base de datos es reportado, el cual incluye 29 señas estáticas y dinámicas. Para cada seña, se capturan los cuadros de imagen RGB así como el mapa de profundidad, mediante sensores RGB-D. Adicionalmente, con el fin de poder proveer muestras sintéticas, una mano robótica virtual ha sido creada para presentar las configuraciones dactilológicas. Un conjunto reducido de 7 señas ha sido seleccionado para su presentación y análisis, en un simulador robótico.

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“…All the CSLR and ISLR datasets that have been discussed above are summarized in Table 1. There are authors in the literature who suggest a system to recognize sign language based on static gestures [5,[14][15][16]. In other words, they use images instead of videos to train their models.…”

Section: Related Workmentioning

confidence: 99%

“…As a result of this, certain authors prefer to create their own datasets. In [5], researchers have generated a dataset for Mexican signs. The Kinect device was used to carry out this task.…”

Section: Introductionmentioning

confidence: 99%

Exploring a Novel Mexican Sign Language Lexicon Video Dataset

Martínez-Sánchez,

Villalón-Turrubiates,

Cervantes-Álvarez

et al. 2023

MTI

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This research explores a novel Mexican Sign Language (MSL) lexicon video dataset containing the dynamic gestures most frequently used in MSL. Each gesture consists of a set of different versions of videos under uncontrolled conditions. The MX-ITESO-100 dataset is composed of a lexicon of 100 gestures and 5000 videos from three participants with different grammatical elements. Additionally, the dataset is evaluated in a two-step neural network model as having an accuracy greater than 99% and thus serves as a benchmark for future training of machine learning models in computer vision systems. Finally, this research provides an inclusive environment within society and organizations, in particular for people with hearing impairments.

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Mexican Sign‐Language Static‐Alphabet Recognition Using 3D Affine Invariants

Cited by 8 publications

References 20 publications

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

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

Hacia la construcción de una base de datos abierta de la LSM

Exploring a Novel Mexican Sign Language Lexicon Video Dataset

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