A Linguistic Feature Vector for the Visual Interpretation of Sign Language
Richard Bowden,Richard Bowden,David Windridge,Timor Kadir,Andrew Zisserman,Michael Brady +5 more
- pp 390-401
TLDR
A novel approach to sign language recognition that provides extremely high classification rates on minimal training data using only single instance training outperforming previous approaches where thousands of training examples are required.Abstract:
This paper presents a novel approach to sign language recognition that provides extremely high classification rates on minimal training data. Key to this approach is a 2 stage classification procedure where an initial classification stage extracts a high level description of hand shape and motion. This high level description is based upon sign linguistics and describes actions at a conceptual level easily understood by humans. Moreover, such a description broadly generalises temporal activities naturally overcoming variability of people and environments. A second stage of classification is then used to model the temporal transitions of individual signs using a classifier bank of Markov chains combined with Independent Component Analysis. We demonstrate classification rates as high as 97.67% for a lexicon of 43 words using only single instance training outperforming previous approaches where thousands of training examples are required.read more
Citations
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Journal ArticleDOI
Gesture Recognition: A Survey
Sushmita Mitra,T. Acharya +1 more
TL;DR: A survey on gesture recognition with particular emphasis on hand gestures and facial expressions is provided, and applications involving hidden Markov models, particle filtering and condensation, finite-state machines, optical flow, skin color, and connectionist models are discussed in detail.
Journal ArticleDOI
A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation
TL;DR: A unified framework for simultaneously performing spatial segmentation, temporal segmentsation, and recognition is introduced and can be applied to continuous image streams where gestures are performed in front of moving, cluttered backgrounds.
Journal ArticleDOI
Continuous Sign Language Recognition: Towards Large Vocabulary Statistical Recognition Systems Handling Multiple Signers
TL;DR: This work presents a statistical recognition approach performing large vocabulary continuous sign language recognition across different signers, and is the first time system design on a large data set with true focus on real-life applicability is thoroughly presented.
Proceedings ArticleDOI
Deep Hand: How to Train a CNN on 1 Million Hand Images When Your Data is Continuous and Weakly Labelled
TL;DR: This work presents a new approach to learning a framebased classifier on weakly labelled sequence data by embedding a CNN within an iterative EM algorithm, which allows the CNN to be trained on a vast number of example images when only loose sequence level information is available for the source videos.
Book ChapterDOI
Sign Language Recognition
TL;DR: This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a precis of sign linguistics and their impact on the field.
References
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Dissertation
Visual Recognition of American Sign Language Using Hidden Markov Models.
TL;DR: Using hidden Markov models (HMM's), an unobstrusive single view camera system is developed that can recognize hand gestures, namely, a subset of American Sign Language (ASL), achieving high recognition rates for full sentence ASL using only visual cues.
Proceedings ArticleDOI
A real-time continuous gesture recognition system for sign language
Rung-Huei Liang,Ming Ouhyoung +1 more
TL;DR: A large vocabulary sign language interpreter is presented with real-time continuous gesture recognition of sign language using a data glove using hidden Markov models for 51 fundamental postures, 6 orientations, and 8 motion primitives.
Book
The Linguistics of British Sign Language: An Introduction
Rachel Sutton-Spence,Bencie Woll +1 more
TL;DR: Conventions used in the text 1. Linguistics and sign linguistics 2. BSL in its social context 3. Constructing sign sentences 4. Questions and negation 5. Mouth patterns and non-manual features in BSL.
Journal ArticleDOI
Glove-Talk: a neural network interface between a data-glove and a speech synthesizer
Sidney Fels,Geoffrey E. Hinton +1 more
TL;DR: To illustrate the potential of multilayer neural networks for adaptive interfaces, a VPL Data-Glove connected to a DECtalk speech synthesizer via five neural networks was used to implement a hand-gesture to speech system, demonstrating that neural networks can be used to develop the complex mappings required in a high bandwidth interface that adapts to the individual user.
Proceedings ArticleDOI
ASL recognition based on a coupling between HMMs and 3D motion analysis
TL;DR: The authors presented a framework for recognizing isolated and continuous American Sign Language (ASL) sentences from three-dimensional data using Hidden Markov Models (HMMs) for recognition, and showed that context-dependent modeling and the coupling of vision methods and HMMs improved the accuracy of continuous ASL recognition.