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In this paper, we propose a machine learning-based multi-stream framework to recognize American Sign Language (ASL) manual signs and nonmanual gestures (face and head movements) in real time from RGB-D videos. Our approach is based on 3D Convolutional Neural Networks (3D CNNs) by fusing the multi-modal features including hand gestures, facial expressions, and body poses from multiple channels (RGB, Depth, Motion, and Skeleton joints). To learn the overall temporal dynamics in a video, a proxy video is generated by selecting a subset of frames for each video which are then used to train the proposed 3D CNN model. We collected a new ASL dataset, ASL-100-RGBD, which contains 42 RGB-D videos captured by a Microsoft Kinect V2 camera. Each video consists of 100 ASL manual signs, along with RGB channel, Depth maps, Skeleton joints, Face features, and HD face. The dataset is fully annotated for each semantic region (i.e. the time duration of each sign that the human signer performs). Our proposed method achieves 92.88% accuracy for recognizing 100 ASL sign glosses in our newly collected ASL-100-RGBD dataset. The effectiveness of our framework for recognizing hand gestures from RGB-D videos is further demonstrated on a large-scale dataset, ChaLearn IsoGD, achieving the state-of-the-art results.
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Creating questionnaires that align with ASL linguistic principles and cultural practices within the Deaf community
Conducting human-centered research by, with, and for the ASL-signing Deaf community, requires rethinking current human-computer interaction processes in order to meet their linguistic and cultural needs and expectations. This paper highlights some key considerations that emerged in our work creating an ASL-based questionnaire, and our recommendations for handling them.
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- Award ID(s):
- 1901026
- PAR ID:
- 10587835
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9781450371032
- Page Range / eLocation ID:
- 1 to 4
- Subject(s) / Keyword(s):
- questionnaires American Sign Language ASL
- Format(s):
- Medium: X
- Location:
- Virtual Event Greece
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3373625.3418071
