Sign Language Recognition (SLR) models rely heavily on advances reached by the Human Action Recognition (HAR). One of the simplest and most dimensional-reduced modality is the skeleton joints and limbs represented with key-point landmarks and edges connecting these landmarks. These skeletons can be obtained by pose estimation, depth maps or motion capture. For HAR, models are usually interested in less granularity of pose estimation, compared to SLR, where it is highly important the landmark estimation of not only the pose and body but the facial gestures, hands and fingers. In this work, we compare three whole-body estimation libraries/models that are gaining attraction in the SLR task. We first find their relation by identifying common keypoints in their landmark structure and analyzing their quality. Then, we complement this analysis by comparing their annotations in three sign language datasets with videos of different quality, background, and region (Peru and USA). We test a sign language recognition model to compare the quality of the annotations provided by these libraries/models.
Cristian Lazo Quispe (Universidad Nacional de Ingenieria)
I am a mechatronic engineering student at the National University of Engineering, with experience in robotics, computer vision, deep learning, machine learning and image/signal processing areas using current cutting-edge techniques. I have worked in projects related to autonomous mobile robot, 2D classification and detection, 2D/3D segmentation, digital signal processing with raspberry pi3 and image generation.
Joe Huamani Malca (PUCP)
Gissella Bejarano Nicho (Baylor University)
Manuel Huaman Ramos (Pontificia Universidad Catolica del Peru)
Pablo Rivas (Marist College)
Tomas Cerny (Baylor University )
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