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Exploring Infant Sensitivity to Visual Language using Eye Tracking and the Preferential Looking Paradigm

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Sign language images dataset from Mexican sign language.

Josué Espejel¹, Laura D Jalili¹, Jair Cervantes¹

¹UAEMEX (Autonomous University of Mexico State), Texcoco 56259, Mexico.

|July 1, 2024

Summary

This study addresses the lack of formal datasets for Mexican Sign Language (MSL) by creating a structured dataset of 249 MSL words. The dataset includes 31,442 images captured using specific visual enhancements for better recognition.

Keywords:

Frame sequences Hand gestures Ideogram Image segmentation

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Area of Science:

Linguistics
Computer Science
Human-Computer Interaction

Background:

Sign languages are complete linguistic systems with unique grammar.
Variations across countries highlight the need for country-specific documentation.
A formal dataset for Mexican Sign Language (MSL) is currently lacking.

Purpose of the Study:

To address the deficit of formal datasets for Mexican Sign Language (MSL).
To structure and create a comprehensive dataset for MSL research and development.

Main Methods:

A dataset of 249 MSL words was structured into 17 subsets.
Videos were recorded using black backgrounds and clothing to enhance focus on hands and face.
An average of 11 individuals per word, with 15 frames per video sequence, yielded 31,442 JPG images.

Main Results:

A novel dataset of 31,442 images for 249 Mexican Sign Language words was successfully created.
The dataset employs visual enhancements (black background/clothing) to improve feature extraction.
Data was collected from multiple individuals to capture sign variations.

Conclusions:

The developed dataset provides a foundational resource for advancing Mexican Sign Language research.
This structured dataset can facilitate the development of MSL recognition technologies.
The dataset's methodology offers a model for documenting other sign languages.