Sign Language Recognition Using Convolutional Neural Networks

Abstract-Sign language is a lingua among the speech and the hearing impaired community. It is hard for most people who are not familiar with sign language to communicate without an interpreter. Sign language recognition appertains to track and recognize the meaningful emotion of human made with fingers, hands, head, arms, face etc. The technique that has been proposed in this work, transcribes the gestures from a sign language to a spoken language which is easily understood by the hearing. The gestures that have been translated include alphabets, words from static images. This becomes more important for the people who completely rely on the gestural sign language for communication tries to communicate with a person who does not understand the sign language. We aim at representing features which will be learned by a technique known as convolutional neural networks (CNN), contains four types of layers: convolution layers, pooling/subsampling layers, non-linear layers, and fully connected layers. The new representation is expected to capture various image features and complex non-linear feature interactions. A softmax layer will be used to recognize signs

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

Computational Models for the Automatic Learning and Recognition of Irish Sign Language

This thesis presents a framework for the automatic recognition of Sign Language sentences. In previous sign language recognition works, the issues of; user independent recognition, movement epenthesis modeling and automatic or weakly supervised training have not been fully addressed in a single recognition framework. This work presents three main contributions in order to address these issues. The first contribution is a technique for user independent hand posture recognition. We present a novel eigenspace Size Function feature which is implemented to perform user independent recognition of sign language hand postures. The second contribution is a framework for the classification and spotting of spatiotemporal gestures which appear in sign language. We propose a Gesture Threshold Hidden Markov Model (GT-HMM) to classify gestures and to identify movement epenthesis without the need for explicit epenthesis training. The third contribution is a framework to train the hand posture and spatiotemporal models using only the weak supervision of sign language videos and their corresponding text translations. This is achieved through our proposed Multiple Instance Learning Density Matrix algorithm which automatically extracts isolated signs from full sentences using the weak and noisy supervision of text translations. The automatically extracted isolated samples are then utilised to train our spatiotemporal gesture and hand posture classifiers. The work we present in this thesis is an important and significant contribution to the area of natural sign language recognition as we propose a robust framework for training a recognition system without the need for manual labeling

Tools for expressive gesture recognition and mapping in rehearsal and performance

Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 97-101).As human movement is an incredibly rich mode of communication and expression, performance artists working with digital media often use performers' movement and gestures to control and shape that digital media as part of a theatrical, choreographic, or musical performance. In my own work, I have found that strong, semantically-meaningful mappings between gesture and sound or visuals are necessary to create compelling performance interactions. However, the existing systems for developing mappings between incoming data streams and output media have extremely low-level concepts of "gesture." The actual programming process focuses on low-level sensor data, such as the voltage values of a particular sensor, which limits the user in his or her thinking process, requires users to have significant programming experience, and loses the expressive, meaningful, and metaphor-rich content of the movement. To remedy these difficulties, I have created a new framework and development environment for gestural control of media in rehearsal and performance, allowing users to create clear and intuitive mappings in a simple and flexible manner by using high-level descriptions of gestures and of gestural qualities. This approach, the Gestural Media Framework, recognizes continuous gesture and translates Laban Effort Notation into the realm of technological gesture analysis, allowing for the abstraction and encapsulation of sensor data into movement descriptions. As part of the evaluation of this system, I choreographed four performance pieces that use this system throughout the performance and rehearsal process to map dancers' movements to manipulation of sound and visual elements. This work has been supported by the MIT Media Laboratory.by Elena Naomi Jessop.S.M

A Framework for Continuous Multimodal Sign Language Recognition

We present a multimodal system for the recognition of manual signs and non-manual signals within continuous sign language sentences. In sign language, information is mainly conveyed through hand gestures (Manual Signs). Non-manual signals, such as facial expressions, head movements, body postures and torso movements, are used to express a large part of the grammar and some aspects of the syntax of sign language. In this paper we propose a multichannel HMM based system to recognize manual signs and non-manual signals. We choose a single non-manual signal, head movement, to evaluate our framework when recognizing non-manual signals. Manual signs and non-manual signals are processed independently using continuous multidimensional HMMs and a HMM threshold model. Experiments conducted demonstrate that our system achieved a detection ratio of 0.95 and a reliability measure of 0.93