Isolated Sign Language Recognition based on Tree Structure Skeleton Images

• URL: http://arxiv.org/abs/2304.05403v1
• Date: Mon, 10 Apr 2023 01:58:50 GMT
• Title: Isolated Sign Language Recognition based on Tree Structure Skeleton Images
• Authors: David Laines, Gissella Bejarano, Miguel Gonzalez-Mendoza, Gilberto Ochoa-Ruiz
• Abstract summary: We use Tree Dense Structure Image (TSSI) as an alternative input to improve the accuracy of skeleton-based models for sign recognition. We trained a SignNet-121 using this type of input and compared it with other skeleton-based deep learning methods. Our model (SL-TSSI-DenseNet) overcomes the state-of-the-art of other skeleton-based models.
• Score: 2.179313476241343
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sign Language Recognition (SLR) systems aim to be embedded in video stream platforms to recognize the sign performed in front of a camera. SLR research has taken advantage of recent advances in pose estimation models to use skeleton sequences estimated from videos instead of RGB information to predict signs. This approach can make HAR-related tasks less complex and more robust to diverse backgrounds, lightning conditions, and physical appearances. In this work, we explore the use of a spatio-temporal skeleton representation such as Tree Structure Skeleton Image (TSSI) as an alternative input to improve the accuracy of skeleton-based models for SLR. TSSI converts a skeleton sequence into an RGB image where the columns represent the joints of the skeleton in a depth-first tree traversal order, the rows represent the temporal evolution of the joints, and the three channels represent the (x, y, z) coordinates of the joints. We trained a DenseNet-121 using this type of input and compared it with other skeleton-based deep learning methods using a large-scale American Sign Language (ASL) dataset, WLASL. Our model (SL-TSSI-DenseNet) overcomes the state-of-the-art of other skeleton-based models. Moreover, when including data augmentation our proposal achieves better results than both skeleton-based and RGB-based models. We evaluated the effectiveness of our model on the Ankara University Turkish Sign Language (TSL) dataset, AUTSL, and a Mexican Sign Language (LSM) dataset. On the AUTSL dataset, the model achieves similar results to the state-of-the-art of other skeleton-based models. On the LSM dataset, the model achieves higher results than the baseline. Code has been made available at: https://github.com/davidlainesv/SL-TSSI-DenseNet.

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