American sign language recognition with the kinect

By Zahoor Zafrulla, Helene Brashear, Thad Starner, Harley Hamilton, Peter Presti. International Conference on Multimodal Interaction, 2011

394 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Developmental and Educational Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/a0ffa0a367b6fdfbff1e8ddbbe48b77a0a361f45

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Meredith Ringel Morris
2. Jesus Suarez
3. Xin Yu
4. Hernisa Kacorri
5. Robin Roberson Murphy
6. Naomi K. Caselli
7. Hamid Reza Vaezi Joze
8. Xilin Chen

In Developmental and Educational Psychology

1. Hamid Reza Vaezi Joze
2. Sergio Escalera
3. Isabelle M Guyon
4. Jesus Suarez
5. Vassilis Athitsos
6. Robin Roberson Murphy
7. Christian Vogler
8. Naomi K. Caselli

In Human-Computer Interaction

1. Sergio Escalera
2. Isabelle M Guyon
3. Hamid Reza Vaezi Joze
4. Vassilis Athitsos
5. Zicheng Liu
6. Hong Cheng
7. Lu Yang
8. Meredith Ringel Morris

Citations in the corpus

• Two streams Recurrent Neural Networks for Large-Scale Continuous Gesture Recognition (2016, methodology): “Therefore, much more work on gesture or sign language recognition is explored on such RGB-D data [1] [2].”
• Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective (2019, methodology): “To address the three-dimensionality of signs, some vision-based approaches use depth cameras [113, 123], multiple cameras [16] or triangulation for 3D reconstruction [99, 98].”
• MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language (2018, background): “Because the sign is performed in 3-dimensions, many vision based approaches use depth information [39, 63, 71] or multiple cameras [4].”
• Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison (2019, background): “Existing word-level sign recognition models are mainly trained and evaluated on either private [26, 38, 78, 28, 49] or small-scale datasets with less than one hundred words [26, 38, 78, 28, 49, 42, 47, 71].”
• Hand gesture recognition with depth images: A review (2012, methodology): “Wang [37], Tang [12], Yang [13], Hassani [18], and Zafrulla [26] all use HMMs in a straightforward manner for gesture classification.”
• Survey on 3D Hand Gesture Recognition (2016, background): “With the power of Kinect skeleton tracking libraries, 3D hand motion-related sign language vocabularies have been successfully recognized and translated for English [57] and Chinese languages [58].”
• Challenges in multimodal gesture recognition (2016, background): “Several approaches have been published in recent years that use depth cameras to improve accuracy in sign language recognition Conly et al. (2015); Wang et al. (2015a); Zafrulla et al. (2011).”