Book ChapterDOI
Sign language recognition using sub-units
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This paper discusses sign language recognition using linguistic sub-units, presenting three types of sub- units for consideration; those learnt from appearance data as well as those inferred from both 2D or 3D tracking data.Abstract:
This paper discusses sign language recognition using linguistic sub-units. It presents three types of sub-units for consideration; those learnt from appearance data as well as those inferred from both 2D or 3D tracking data. These sub-units are then combined using a sign level classifier; here, two options are presented. The first uses Markov Models to encode the temporal changes between sub-units. The second makes use of Sequential Pattern Boosting to apply discriminative feature selection at the same time as encoding temporal information. This approach is more robust to noise and performs well in signer independent tests, improving results from the 54% achieved by the Markov Chains to 76%.read more
Citations
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Depth-Based Hand Pose Estimation: Methods, Data, and Challenges
TL;DR: An extensive analysis of the state-of-the-art, focusing on hand pose estimation from a single depth frame, defines a consistent evaluation criteria, rigorously motivated by human experiments and introduces a simple nearest-neighbor baseline that outperforms most existing systems.
Proceedings ArticleDOI
Using Convolutional 3D Neural Networks for User-independent continuous gesture recognition
TL;DR: An end-to-end deep network is trained for continuous gesture recognition (jointly learning both the feature representation and the classifier) that performs three-dimensional convolutions to extract features related to both the appearance and motion from volumes of color frames.
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Multi-layered gesture recognition with Kinect
TL;DR: The essential linguistic characters of gestures: the components concurrent character and the sequential organization character are explored in a multi-layered framework, which extracts features from both the segmented semantic units and the whole gesture sequence and then sequentially classifies the motion, location and shape components.
Journal ArticleDOI
AUTSL: A Large Scale Multi-Modal Turkish Sign Language Dataset and Baseline Methods
TL;DR: This study presents a new large-scale multi-modal Turkish Sign Language dataset (AUTSL) with a benchmark and provides baseline models for performance evaluations and used Convolutional Neural Networks to extract features, unidirectional and bidirectional Long Short-Term Memory models to characterize temporal information and incorporated feature pooling modules and temporal attention to improve the performances.
Journal ArticleDOI
Review: Feature extraction in Brazilian Sign Language Recognition based on phonological structure and using RGB-D sensors
Sílvia Grasiella Moreira Almeida,Sílvia Grasiella Moreira Almeida,Frederico Gadelha Guimarães,Jaime A. Ramírez +3 more
TL;DR: A methodology for feature extraction in Brazilian Sign Language (BSL) that explores the phonological structure of the language and relies on RGB-D sensor for obtaining intensity, position and depth data and employs Support Vector Machines to classify signs based on these features and linguistic elements.
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