A vision-based sign language recognition system using tied-mixture density HMM

Zhang, Liang-Guo; Chen, Yiqiang; Fang, Gaolin; Chen, Xilin; Gao, Wen

doi:10.1145/1027933.1027967

Cited by 59 publications

(36 citation statements)

References 19 publications

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“…The research in SL recognition has been conducted for different SLs in different countries [12][13]15] and SL variants [5]. Many research in SL recognition only depicted the output of system and did not include the comprehensive dataset in the results.…”

Section: Work On Constructing Databases For Sl Recognitionmentioning

confidence: 99%

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Malaysian sign language dataset for automatic sign language recognition system

Karbasi¹,

Zabidi²,

Yassin³

et al. 2018

J. Fundam and Appl Sci.

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Hearing impaired individuals have issues to communicate with normal people. They have their own language called Sign Language (SL) to express their feeling or to communi with others. As communication is an essential part of normal everyday life, it is particularly important for deaf people to communicate as normally as possible with others. Recent advancements in computing technologies have the potential to be applied recognition. These computer language and vice-versa. This paper describes the development of a dataset for an automated SL recognition system based on the Malaysian Sign Language are described.

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Section: Work On Constructing Databases For Sl Recognitionmentioning

confidence: 99%

“…Many research in SL recognition only depicted the output of system and did not include the comprehensive dataset in the results. Several references that include the dataset applications are in [6,9,14], while in [15] presented two variants of the ASL (Greek and British).…”

Section: Work On Constructing Databases For Sl Recognitionmentioning

confidence: 99%

Malaysian sign language dataset for automatic sign language recognition system

Karbasi¹,

Zabidi²,

Yassin³

et al. 2018

J. Fundam and Appl Sci.

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“…Thus, Liang-Guo Zhang et al [61] advanced a sign language recognition system based on glove colors, as shown in Figure 2.19. Their left-hand glove is purple, while each finger of the right-hand glove has a specific color (red, yellow, orange, blue and green).…”

Section: Contour and Color Based Approachesmentioning

confidence: 99%

Development of a haptic video chat system

Zhang

Saboune

Saddik

2014

Multimed Tools Appl

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Since conventional audio-video teleconferencing systems have reached their limits, the needs of integrating new sensations to improve users' telecommunication experience are growing. Haptics, the sense of touch, which includes handshake, comforting hug, encouraging pat, and other physical contacts, is of great importance for interpersonal communication, since it allows people to express and receive intimate affection, intention or emotion efficiently. Motivated by a few haptic telecommunication softwares, this thesis presents an innovative webcam-based touchscreen to replace the haptic device, which is used in HugMe [30] [26][27] system as human hand, to further increase people's degree of immersion with the audio-video-haptic teleconferencing system at a more reasonable price. With our webcam-based touchscreen, the user could directly use his/her bare hand to touch the image of the person who he/she is chatting with, instead of controlling a haptic device as the medium to realize indirect touch. This thesis also gives details of the touchscreen method and our proposed mathematical models for touch position calculation. Experimental results show that our system is accurate and robust, while maintaining high compatibility with conventional audio-video teleconferencing systems for combination. With our haptic jacket, the passive user can feel the touch of the active user at the right position. Also, additional applications with the touchscreen, such as writing and drawing, are developed and tested. Finally, we draw the conclusions and talk about future work.ii

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“…In some cases, the gloves used were designed so that the hand pose could be better detected; employing coloured markers such as Holden and Owens [46] or different coloured fingers [44]. Zhang et al [114] made use of multicoloured gloves (where the fingers and palms of the hands were different colours) and used the hands geometry to detect both position and shape. Using coloured gloves reduces the encumbrance to the signer but does not remove it completely.…”

Section: Tracking Basedmentioning

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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A vision-based sign language recognition system using tied-mixture density HMM

Cited by 59 publications

References 19 publications

Malaysian sign language dataset for automatic sign language recognition system

Malaysian sign language dataset for automatic sign language recognition system

Development of a haptic video chat system

Sign Language Recognition

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