Spelling it out: Real-time ASL fingerspelling recognition
By Nicolas Pugeault, Richard Bowden. IEEE International Conference on Computer Vision, 2011
429 citations recorded by the source corpus.
This article presents an interactive hand shape recognition user interface for American Sign Language (ASL) finger-spelling. The system makes use of a Microsoft Kinect device to collect appearance and depth images, and of the OpenNI+NITE framework for hand detection and tracking. Hand-shapes corresponding to letters of the alphabet are characterized using appearance and depth images and classified using random forests. We compare classification using appearance and depth images, and show a combination of both lead to best results, and validate on a dataset of four different users. This hand shape detection works in real-time and is integrated in an interactive user interface allowing the signer to select between ambiguous detections and integrated with an English dictionary for efficient writing.
Subfields: Developmental and Educational Psychology, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/40028fa551c445ce8ecb43523165e40160b7ffa6
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In Developmental and Educational Psychology
In Human-Computer Interaction
- Sergio Escalera
- Isabelle M Guyon
- Martin Saerbeck
- P. PramodKumar
- Vassilis Athitsos
- Liang Wang
- Hongsong Wang
Citations in the corpus
- Feature learning based on SAE-PCA network for human gesture recognition in RGBD images (2015, methodology): “Pugeault et al. [1] proposed an interactive hand shape recognition system including hand segmentation, hand tracking, and hand gesture recognition.”
- Challenges in multimodal gesture recognition (2016, background): “Most recently, due to the advent of commercially available depth sensors, there is an increased interest in methods relying on depth data (Keskin et al., 2012; Mo and Neumann, 2006; Oikonomidis et al., 2011; Pugeault and Bowden, 2011; Lopes et al., 2014).”
- Recent methods and databases in vision-based hand gesture recognition: A review (2015, methodology): “22 24 classes, 9 subjects, 65,000 samples S [141]”