Review on Classification Methods used in Image based Sign Language Recognition System

Sign language is the way of communication among the Deaf-Dumb people by expressing signs. This paper is present review on Sign language Recognition system that aims to provide communication way for Deaf and Dumb pople. This paper describes review of Image based sign language recognition system. Signs are in the form of hand gestures and these gestures are identified from images as well as videos. Gestures are identified and classified according to features of Gesture image. Features are like shape, rotation, angle, pixels, hand movement etc. Features are finding by various Features Extraction methods and classified by various machine learning methods. Main pupose of this paper is to review on classification methods of similar systems used in Image based hand gesture recognition . This paper also describe comarison of various system on the base of classification methods and accuracy rate

A vision-based approach for human hand tracking and gesture recognition.

Hand gesture interface has been becoming an active topic of human-computer interaction (HCI). The utilization of hand gestures in human-computer interface enables human operators to interact with computer environments in a natural and intuitive manner. In particular, bare hand interpretation technique frees users from cumbersome, but typically required devices in communication with computers, thus offering the ease and naturalness in HCI. Meanwhile, virtual assembly (VA) applies virtual reality (VR) techniques in mechanical assembly. It constructs computer tools to help product engineers planning, evaluating, optimizing, and verifying the assembly of mechanical systems without the need of physical objects. However, traditional devices such as keyboards and mice are no longer adequate due to their inefficiency in handling three-dimensional (3D) tasks. Special VR devices, such as data gloves, have been mandatory in VA. This thesis proposes a novel gesture-based interface for the application of VA. It develops a hybrid approach to incorporate an appearance-based hand localization technique with a skin tone filter in support of gesture recognition and hand tracking in the 3D space. With this interface, bare hands become a convenient substitution of special VR devices. Experiment results demonstrate the flexibility and robustness introduced by the proposed method to HCI.Dept. of Computer Science. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2004 .L8. Source: Masters Abstracts International, Volume: 43-03, page: 0883. Adviser: Xiaobu Yuan. Thesis (M.Sc.)--University of Windsor (Canada), 2004

Vision-based hand shape identification for sign language recognition

This thesis introduces an approach to obtain image-based hand features to accurately describe hand shapes commonly found in the American Sign Language. A hand recognition system capable of identifying 31 hand shapes from the American Sign Language was developed to identify hand shapes in a given input image or video sequence. An appearance-based approach with a single camera is used to recognize the hand shape. A region-based shape descriptor, the generic Fourier descriptor, invariant of translation, scale, and orientation, has been implemented to describe the shape of the hand. A wrist detection algorithm has been developed to remove the forearm from the hand region before the features are extracted. The recognition of the hand shapes is performed with a multi-class Support Vector Machine. Testing provided a recognition rate of approximately 84% based on widely varying testing set of approximately 1,500 images and training set of about 2,400 images. With a larger training set of approximately 2,700 images and a testing set of approximately 1,200 images, a recognition rate increased to about 88%

Auto clustering for unsupervised learning of atomic gesture components using minimum description length

We present an approach to automatically segment and label a continuous observation sequence of hand gestures for a complete unsupervised model acquisition. The method is based on the assumption that gestures can be viewed as repetitive sequences of atomic components, similar to phonemes in speech, governed by a high level structure controlling the temporal sequence. We show that the generating process for the atomic components can be described in gesture space by a mixture of Gaussian, with each mixture component tied to one atomic behaviour. Mixture components are determined using a standard EM approach while the determination of the number of components is based on an information criteria, the Minimum Description Length

A random forest approach to segmenting and classifying gestures

This thesis investigates a gesture segmentation and recognition scheme that employs a random forest classification model. A complete gesture recognition system should localize and classify each gesture from a given gesture vocabulary, within a continuous video stream. Thus, the system must determine the start and end points of each gesture in time, as well as accurately recognize the class label of each gesture. We propose a unified approach that performs the tasks of temporal segmentation and classification simultaneously. Our method trains a random forest classification model to recognize gestures from a given vocabulary, as presented in a training dataset of video plus 3D body joint locations, as well as out-of-vocabulary (non-gesture) instances. Given an input video stream, our trained model is applied to candidate gestures using sliding windows at multiple temporal scales. The class label with the highest classifier confidence is selected, and its corresponding scale is used to determine the segmentation boundaries in time. We evaluated our formulation in segmenting and recognizing gestures from two different benchmark datasets: the NATOPS dataset of 9,600 gesture instances from a vocabulary of 24 aircraft handling signals, and the CHALEARN dataset of 7,754 gesture instances from a vocabulary of 20 Italian communication gestures. The performance of our method compares favorably with state-of-the-art methods that employ Hidden Markov Models or Hidden Conditional Random Fields on the NATOPS dataset. We conclude with a discussion of the advantages of using our model

Portuguese sign language recognition via computer vision and depth sensor

Sign languages are used worldwide by a multitude of individuals. They are mostly used by the deaf communities and their teachers, or people associated with them by ties of friendship or family. Speakers are a minority of citizens, often segregated, and over the years not much attention has been given to this form of communication, even by the scientific community. In fact, in Computer Science there is some, but limited, research and development in this area. In the particular case of sign Portuguese Sign Language-PSL that fact is more evident and, to our knowledge there isn’t yet an efficient system to perform the automatic recognition of PSL signs. With the advent and wide spreading of devices such as depth sensors, there are new possibilities to address this problem. In this thesis, we have specified, developed, tested and preliminary evaluated, solutions that we think will bring valuable contributions to the problem of Automatic Gesture Recognition, applied to Sign Languages, such as the case of Portuguese Sign Language. In the context of this work, Computer Vision techniques were adapted to the case of Depth Sensors. A proper gesture taxonomy for this problem was proposed, and techniques for feature extraction, representation, storing and classification were presented. Two novel algorithms to solve the problem of real-time recognition of isolated static poses were specified, developed, tested and evaluated. Two other algorithms for isolated dynamic movements for gesture recognition (one of them novel), have been also specified, developed, tested and evaluated. Analyzed results compare well with the literature.As Línguas Gestuais são utilizadas em todo o Mundo por uma imensidão de indivíduos. Trata-se na sua grande maioria de surdos e/ou mudos, ou pessoas a eles associados por laços familiares de amizade ou professores de Língua Gestual. Tratando-se de uma minoria, muitas vezes segregada, não tem vindo a ser dada ao longo dos anos pela comunidade científica, a devida atenção a esta forma de comunicação. Na área das Ciências da Computação existem alguns, mas poucos trabalhos de investigação e desenvolvimento. No caso particular da Língua Gestual Portuguesa - LGP esse facto é ainda mais evidente não sendo nosso conhecimento a existência de um sistema eficaz e efetivo para fazer o reconhecimento automático de gestos da LGP. Com o aparecimento ou massificação de dispositivos, tais como sensores de profundidade, surgem novas possibilidades para abordar este problema. Nesta tese, foram especificadas, desenvolvidas, testadas e efectuada a avaliação preliminar de soluções que acreditamos que trarão valiosas contribuições para o problema do Reconhecimento Automático de Gestos, aplicado às Línguas Gestuais, como é o caso da Língua Gestual Portuguesa. Foram adaptadas técnicas de Visão por Computador ao caso dos Sensores de Profundidade. Foi proposta uma taxonomia adequada ao problema, e apresentadas técnicas para a extração, representação e armazenamento de características. Foram especificados, desenvolvidos, testados e avaliados dois algoritmos para resolver o problema do reconhecimento em tempo real de poses estáticas isoladas. Foram também especificados, desenvolvidos, testados e avaliados outros dois algoritmos para o Reconhecimento de Movimentos Dinâmicos Isolados de Gestos(um deles novo).Os resultados analisados são comparáveis à literatura.Las lenguas de Signos se utilizan en todo el Mundo por una multitud de personas. En su mayoría son personas sordas y/o mudas, o personas asociadas con ellos por vínculos de amistad o familiares y profesores de Lengua de Signos. Es una minoría de personas, a menudo segregadas, y no se ha dado en los últimos años por la comunidad científica, la atención debida a esta forma de comunicación. En el área de Ciencias de la Computación hay alguna pero poca investigación y desarrollo. En el caso particular de la Lengua de Signos Portuguesa - LSP, no es de nuestro conocimiento la existencia de un sistema eficiente y eficaz para el reconocimiento automático. Con la llegada en masa de dispositivos tales como Sensores de Profundidad, hay nuevas posibilidades para abordar el problema del Reconocimiento de Gestos. En esta tesis se han especificado, desarrollado, probado y hecha una evaluación preliminar de soluciones, aplicada a las Lenguas de Signos como el caso de la Lengua de Signos Portuguesa - LSP. Se han adaptado las técnicas de Visión por Ordenador para el caso de los Sensores de Profundidad. Se propone una taxonomía apropiada para el problema y se presentan técnicas para la extracción, representación y el almacenamiento de características. Se desarrollaran, probaran, compararan y analizan los resultados de dos nuevos algoritmos para resolver el problema del Reconocimiento Aislado y Estático de Posturas. Otros dos algoritmos (uno de ellos nuevo) fueran también desarrollados, probados, comparados y analizados los resultados, para el Reconocimiento de Movimientos Dinámicos Aislados de los Gestos