Hand pose estimation for american sign language recognition

Isaacs, Jason C.; Foo, Simon Y.

doi:10.1109/ssst.2004.1295634

Cited by 45 publications

(21 citation statements)

References 2 publications

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“…Finally, for a finger-spelling user interface to be usable in practice, it needs to run in real-time (> 10Hz) on commonly available computers. The most similar previous work is probably the article by Isaac & Foo [6], who proposed an ASL finger-spelling recognition system based on neural networks applied to wavelets features. As in this article, they focus on static hand shapes, and report a recognition rate of 99% but do not specify the size of the dataset and number of different subjects.…”

Section: Introductionmentioning

confidence: 99%

Spelling it out: Real-time ASL fingerspelling recognition

Pugeault

Bowden

2011

2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)

332

215

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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.

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Section: Introductionmentioning

confidence: 99%

Spelling it out: Real-time ASL fingerspelling recognition

Pugeault

Bowden

2011

2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops)

332

215

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“…Isaacs and Foo developed a similar hand pose recognition system that relied on wavelet decomposition for feature extraction and neural networks for classification [24]. They developed the system to represent around 2000 sign language sentences and phrases using a set of eighty words.…”

Section: Previous Workmentioning

confidence: 99%

Dynamic Hand Gesture Recognition Framework

Premaratne

Yang

Zhou³

et al. 2014

Intelligent Computing Methodologies

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Abstract. Sign languages originated long before any speech-based languages evolved in the world. They contain subtleties that rival any speech-based languages conveying a rich source of information much faster than any speechbased languages. Similar to the diversity of speech-based languages, sign languages vary from region to region. However, unlike the speech counterpart, sign languages from diverse regions from the world have much common traits that originate from human evolution. Researchers have been intrigued by these common traits and have always wondered whether sign language-type communication is possible for instructing the computers opposed to the mundane keyboard and mouse. This trend is popularly known as Human Computer Interaction (HCI) and has used a subset of common sign language hand gestures to interact with machines through computer vision.Since the sign languages comprise of thousands of subtle gestures, a new sophisticated approach has to be initiated for eventual recognition of vast number of gestures. Hand gestures comprise of both static postures and dynamic gestures and can carry significantly rich vocabulary describing words in the thousands. In this article, we present our latest research that describes a mechanism to accurately interpret dynamic hand gestures using a concept known as 'gesture primitives' where each dynamic gesture is described as a collection of many primitives over time that can drive a classification strategy based on Hidden Markov Model to reliably predict the gesture using statistical knowledge of such gestures. We believe that even though our work is in its infancy, this strategy can be extended to thousands of dynamic gestures used in sign language to be interpreted by machines.

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“…Isaacs and Foo [50] worked on finger spelling using wavelet features to detect static hand shapes. This approach limited them to non-dynamic alphabets.…”

Section: Finger Spellingmentioning

confidence: 99%

Sign Language Recognition

Cooper

Holt

Bowden

2011

Visual Analysis of Humans

176

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This chapter covers the key aspects of Sign Language Recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data sets.

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Hand pose estimation for american sign language recognition

Cited by 45 publications

References 2 publications

Spelling it out: Real-time ASL fingerspelling recognition

Spelling it out: Real-time ASL fingerspelling recognition

Dynamic Hand Gesture Recognition Framework

Sign Language Recognition

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