GCTW Alignment for isolated gesture recognition

In recent years, there has been increasing interest in developing automatic Sign Language Recognition (SLR) systems because Sign Language (SL) is the main mode of communication between deaf people all over the world. However, most people outside the deaf community do not understand SL, generating a communication problem, between both communities. Recognizing signs is a challenging problem because manual signing (not taking into account facial gestures) has four components that have to be recognized, namely, handshape, movement, location and palm orientation. Even though the appearance and meaning of basic signs are well-defined in sign language dictionaries, in practice, many variations arise due to different factors like gender, age, education or regional, social and ethnic factors which can lead to significant variations making hard to develop a robust SL recognition system. This project attempts to introduce the alignment of videos into isolated SLR, given that this approach has not been studied deeply, even though it presents a great potential for correctly recognize isolated gestures. We also aim for a user-independent recognition, which means that the system should give have a good recognition accuracy for the signers that were not represented in the data set. The main features used for the alignment are the wrists coordinates that we extracted from the videos by using OpenPose. These features will be aligned by using Generalized Canonical Time Warping. The resultant videos will be classified by making use of a 3D CNN. Our experimental results show that the proposed method has obtained a 65.02% accuracy, which places us 5th in the 2017 Chalearn LAP isolated gesture recognition challenge, only 2.69% away from the first place.Trabajo de investigació

Data mining and modelling for sign language

Sign languages have received significantly less attention than spoken languages in the research areas of corpus analysis, machine translation, recognition, synthesis and social signal processing, amongst others. This is mainly due to signers being in a clear minority and there being a strong prior belief that sign languages are simply arbitrary gestures. To date, this manifests in the insufficiency of sign language resources available for computational modelling and analysis, with no agreed standards and relatively stagnated advancements compared to spoken language interaction research. Fortunately, the machine learning community has developed methods, such as transfer learning, for dealing with sparse resources, while data mining techniques, such as clustering can provide insights into the data. The work described here utilises such transfer learning techniques to apply neural language model to signed utterances and to compare sign language phonemes, which allows for clustering of similar signs, leading to automated annotation of sign language resources. This thesis promotes the idea that sign language research in computing should rely less on hand-annotated data thus opening up the prospect of using readily available online data (e.g. signed song videos) through the computational modelling and automated annotation techniques presented in this thesis