Facial Feature Tracking and Occlusion Recovery in American Sign Language

Facial features play an important role in expressing grammatical information in signed languages, including American Sign Language(ASL). Gestures such as raising or furrowing the eyebrows are key indicators of constructions such as yes-no questions. Periodic head movements (nods and shakes) are also an essential part of the expression of syntactic information, such as negation (associated with a side-to-side headshake). Therefore, identification of these facial gestures is essential to sign language recognition. One problem with detection of such grammatical indicators is occlusion recovery. If the signer's hand blocks his/her eyebrows during production of a sign, it becomes difficult to track the eyebrows. We have developed a system to detect such grammatical markers in ASL that recovers promptly from occlusion. Our system detects and tracks evolving templates of facial features, which are based on an anthropometric face model, and interprets the geometric relationships of these templates to identify grammatical markers. It was tested on a variety of ASL sentences signed by various Deaf native signers and detected facial gestures used to express grammatical information, such as raised and furrowed eyebrows as well as headshakes.National Science Foundation (IIS-0329009, IIS-0093367, IIS-9912573, EIA-0202067, EIA-9809340

GART: The Gesture and Activity Recognition Toolkit

Presented at the 12th International Conference on Human-Computer Interaction, Beijing, China, July 2007.The original publication is available at www.springerlink.comThe Gesture and Activity Recognition Toolit (GART) is a user interface toolkit designed to enable the development of gesture-based applications. GART provides an abstraction to machine learning algorithms suitable for modeling and recognizing different types of gestures. The toolkit also provides support for the data collection and the training process. In this paper, we present GART and its machine learning abstractions. Furthermore, we detail the components of the toolkit and present two example gesture recognition applications

A new framework for sign language recognition based on 3D handshape identification and linguistic modeling

Current approaches to sign recognition by computer generally have at least some of the following limitations: they rely on laboratory conditions for sign production, are limited to a small vocabulary, rely on 2D modeling (and therefore cannot deal with occlusions and off-plane rotations), and/or achieve limited success. Here we propose a new framework that (1) provides a new tracking method less dependent than others on laboratory conditions and able to deal with variations in background and skin regions (such as the face, forearms, or other hands); (2) allows for identification of 3D hand configurations that are linguistically important in American Sign Language (ASL); and (3) incorporates statistical information reflecting linguistic constraints in sign production. For purposes of large-scale computer-based sign language recognition from video, the ability to distinguish hand configurations accurately is critical. Our current method estimates the 3D hand configuration to distinguish among 77 hand configurations linguistically relevant for ASL. Constraining the problem in this way makes recognition of 3D hand configuration more tractable and provides the information specifically needed for sign recognition. Further improvements are obtained by incorporation of statistical information about linguistic dependencies among handshapes within a sign derived from an annotated corpus of almost 10,000 sign tokens

Gesture recognition using position and appearance features

In this paper a scheme for recognizing hand gestures is presented using the output of a condensation tracker. The tracker is used to obtain a set of features. These features consisting of temporal evolution of the spatial moments form high dimensional feature vectors. The principal components of the feature trajectories are used to recognize the gestures

Automatic recognition of fingerspelled words in British Sign Language

We investigate the problem of recognizing words from video, fingerspelled using the British Sign Language (BSL) fingerspelling alphabet. This is a challenging task since the BSL alphabet involves both hands occluding each other, and contains signs which are ambiguous from the observer’s viewpoint. The main contributions of our work include: (i) recognition based on hand shape alone, not requiring motion cues; (ii) robust visual features for hand shape recognition; (iii) scalability to large lexicon recognition with no re-training. We report results on a dataset of 1,000 low quality webcam videos of 100 words. The proposed method achieves a word recognition accuracy of 98.9%

Multimedia Dictionary and Synthesis of Sign Language

We developed a multimedia dictionary of the Slovenian Sign Language (SSL) which consists of words, illustrations and video clips. We describe the structure of the dictionary and give examples of its user interface. Based on our sign language dictionary, we developed a method of synthesizing the sign language by intelligent joining of video clips, which makes possible a translation of written texts or, in connection with a speech recognition system, of spoken words to the sign language

Sign Language Recognition: Working with Limited Corpora

The availability of video format sign language corpora limited. This leads to a desire for techniques which do not rely on large, fully-labelled datasets. This paper covers various methods for learning sign either from small data sets or from those without ground truth labels. To avoid non-trivial tracking issues; sign detection is investigated using volumetric spatio-temporal features. Following this the advantages of recognising the component parts of sign rather than the signs themselves is demonstrated and finally the idea of using a weakly labelled data set is considered and results shown for work in this area

Minimal Training, Large Lexicon, Unconstrained Sign Language Recognition

This paper presents a flexible monocular system capable of recognising sign lexicons far greater in number than previous approaches. The power of the system is due to four key elements: (i) Head and hand detection based upon boosting which removes the need for temperamental colour segmentation; (ii) A body centred description of activity which overcomes issues with camera placement, calibration and user; (iii) A two stage classification in which stage I generates a high level linguistic description of activity which naturally generalises and hence reduces training; (iv) A stage II classifier bank which does not require HMMs, further reducing training requirements. The outcome of which is a system capable of running in real-time, and generating extremely high recognition rates for large lexicons with as little as a single training instance per sign. We demonstrate classification rates as high as 92% for a lexicon of 164 words with extremely low training requirements outperforming previous approaches where thousands of training examples are required

Sign Language Tutoring Tool

In this project, we have developed a sign language tutor that lets users learn isolated signs by watching recorded videos and by trying the same signs. The system records the user's video and analyses it. If the sign is recognized, both verbal and animated feedback is given to the user. The system is able to recognize complex signs that involve both hand gestures and head movements and expressions. Our performance tests yield a 99% recognition rate on signs involving only manual gestures and 85% recognition rate on signs that involve both manual and non manual components, such as head movement and facial expressions.Comment: eNTERFACE'06. Summer Workshop. on Multimodal Interfaces, Dubrovnik : Croatie (2007