The common practice in sign language recognition is to first construct individual sign models, in terms of discrete state transitions, mostly represented using Hidden Markov Models, from manually isolated sign samples and then to use them to recognize signs in continuous sentences. In this thesis we use a continuous state space model, where the states are based on purely image-based features, without the use of special gloves. We also present an unsupervised approach to both extract and learn models for continuous basic units of signs, which we term as signemes, from continuous sentences. Given a set of sentences with a common sign, we can automatically learn the model for part of the sign,or signeme, that is least affected by coarticulation effects. We tested our idea using the publicly available Boston SignStreamDataset by building signeme models of 18 signs. We test the quality of the models by considering how well we can localize the sign in a new sentence. We also present the concept of smooth continuous curve based models formed using functional splines and curve registration. We illustrate this idea using 16 signs.

Continuous recognition of sign language has many practical applications and it can help to improve the quality of life of deaf persons by facilitating their interaction with hearing populace in public situations. This has led to some research in automated continuous American Sign Language recognition. But most work in continuous ASL recognition has only used top-down Hidden Markov Model (HMM) based approaches for recognition. There is no work on using facial information, which is considered to be fairly important. In this thesis, we explore bottom-up approach based on the use of Relational Distributions and Space of Probability Functions (SoPF) for intermediate level ASL recognition. We also use non-manual information, firstly, to decrease the number of deletion and insertion errors and secondly, to find whether the ASL sentence has 'Negation' in it, for which we use motion trajectories of the face. The experimental results show: The SoPF representation works well for ASL recognition. The accuracy based on the number of deletion errors, considering the 8 most probable signs in the sentence is 95%, while when considering 6 most probable signs, is 88%. Using facial or non-manual information increases accuracy when we consider top 6 signs, from 88% to 92%. Thus face does have information content in it. It is difficult to directly combine the manual information (information from hand motion) with non-manual (facial information) to improve the accuracy because of following two reasons: Manual images are not synchronized with the non-manual images. For example the same facial expressions is not present at the same manual position in two instances of the same sentences. One another problem in finding the facial expresion related with the sign, occurs when there is presence of a strong non-manual indicating 'Assertion' or 'Negation' in the sentence. In such cases the facial expressions are totally dominated by the face movements which is indicated by 'head shakes' or 'head nods'. The number of sentences, that have 'Negation' in them and are correctly recognized with the help of motion trajectories of the face are, 27 out of 30.

Title from PDF of title page. / Document formatted into pages; contains 80 pages. / Thesis (M.S.C.S.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: Continuous recognition of sign language has many practical applications and it can help to improve the quality of life of deaf persons by facilitating their interaction with hearing populace in public situations. This has led to some research in automated continuous American Sign Language recognition. But most work in continuous ASL recognition has only used top-down Hidden Markov Model (HMM) based approaches for recognition. There is no work on using facial information, which is considered to be fairly important. In this thesis, we explore bottom-up approach based on the use of Relational Distributions and Space of Probability Functions (SoPF) for intermediate level ASL recognition. We also use non-manual information, firstly, to decrease the number of deletion and insertion errors and secondly, to find whether the ASL sentence has 'Negation' in it, for which we use motion trajectories of the face. / ABSTRACT: The experimental results show: - The SoPF representation works well for ASL recognition. The accuracy based on the number of deletion errors, considering the 8 most probable signs in the sentence is 95%, while when considering 6 most probable signs, is 88%. - Using facial or non-manual information increases accuracy when we consider top 6 signs, from 88% to 92%. Thus face does have information content in it. - It is difficult to directly combine the manual information (information from hand motion) with non-manual (facial information) to improve the accuracy because of following two reasons: 1. Manual images are not synchronized with the non-manual images. For example the same facial expressions is not present at the same manual position in two instances of the same sentences. 2. One another problem in finding the facial expresion related with the sign, occurs when there is presence of a strong non-manual indicating 'Assertion' or 'Negation' in the sentence. / ABSTRACT: In such cases the facial expressions are totally dominated by the face movements which is indicated by 'head shakes' or 'head nods'. - The number of sentences, that have 'Negation' in them and are correctly recognized with the help of motion trajectories of the face are, 27 out of 30. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.