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Two Dimensional Signal And Image Processing

Hand gesture is one of the most prominent ways of communication since the beginning of the human era. Hand gesture recognition extends human-computer interaction (HCI) more convenient and flexible. Therefore, it is important to identify each character correctly for calm and error-free HCI. Literature survey reveals that most of the existing hand gesture recognition (HGR) systems have considered only a few simple discriminating gestures for recognition performance. This paper applies deep learning-based convolutional neural networks (CNNs) for robust modeling of static signs in the context of sign language recognition. In this work, CNN is employed for HGR where both alphabets and numerals of ASL are considered simultaneously. The pros and cons of CNNs used for HGR are also highlighted. The CNN architecture is based on modified AlexNet and modified VGG16 models for classification. Modified pre-trained AlexNet and modified pre-trained VGG16 based architectures are used for feature extraction followed by a multiclass support vector machine (SVM) classifier. The results are evaluated based on different layer features for best recognition performance. To examine the accuracy of the HGR schemes, both the leave-one-subject-out and a random 70–30 form of cross-validation approach were adopted. This work also highlights the recognition accuracy of each character, and their similarities with identical gestures. The experiments are performed in a simple CPU system instead of high-end GPU systems to demonstrate the cost-effectiveness of this work. The proposed system has achieved a recognition accuracy of 99.82%, which is better than some of the state-of-art methods.

CNN based feature extraction and classification for sign language

Augmentative and Alternative Communication (AAC) aims to complement or replace spoken language to compensate for expression difficulties faced by people with speech impairments. Computing systems have been developed to support AAC; however, partially due to technical problems, poor interface, and limited interaction functions, AAC systems are not widespread, adopted, and used, therefore reaching a limited audience. This article proposes a methodology to support AAC for people with motor impairments, using computer vision and machine learning techniques to allow for personalized gestural interaction. The methodology was applied in a pilot system used by both volunteers without disabilities, and by volunteers with motor and speech impairments, to create datasets with personalized gestures. The created datasets and a public dataset were used to evaluate the technologies employed for gesture recognition, namely the Support Vector Machine (SVM) and Convolutional Neural Network (using Transfer Learning), and for motion representation, namely the conventional Motion History Image and Optical Flow-Motion History Image (OF-MHI). Results obtained from the estimation of prediction error using K-fold cross-validation suggest SVM associated with OF-MHI presents slightly better results for gesture recognition. Results indicate the technical feasibility of the proposed methodology, which uses a low-cost approach, and reveals the challenges and specific needs observed during the experiment with the target audience.

Computer Vision-based Methodology to Improve Interaction for People with Motor and Speech Impairment

Augmentative and Alternative Communication (AAC) involves the use of non-verbal modes as a complement or substitute for spoken language, supporting communicative abilities of people, especially people with speech limitations. Computing systems have been proposed to support AAC, applying different technology to address users' different needs. Computer vision techniques can assist people with motor impairments by using their remaining functional motions. This paper proposes a methodology to support AAC of people with motor impairments, using computer vision and machine learning techniques to enable personalized gestural interaction. The methodology was instantiated in a pilot system described in this paper and evaluated by Human-Computer Interaction experts. The evaluation results suggested improvements for the methodology and for the system, and indicated the methodology is feasible to support the design of AAC systems, and that the developed system is promising to support AAC.

Towards a Methodology to Support Augmentative and Alternative Communication by means of Personalized Gestural Interaction

Technology can support people with disabilities to participate in social and economic life. Using relevant Human-Computer Interaction, as obtained through Intelligent User Interfaces, people with motor and speech impairments may be able to communicate in different ways. Augmentative and Alternative Communication supported by Computer Vision systems can benefit from the recognition of users' remaining functional motions as an alternative interaction design approach. Based on a methodology in which gestures and their meanings are created and configured by users and their caregivers, we developed a Computer Vision system, named PGCA, that employs machine learning techniques to create personalized gestural interaction as an Assistive Technology resource for communication purposes. Using a low-cost approach, PGCA has been experienced with students with motor and speech impairments to create personalized gesture datasets and to identify improvements for the system. This paper presents an experiment carried with the target audience using a game-based approach where three students used PGCA to interact with communication boards and to play a game. The system was evaluated by special education professionals using the System Usability Scale and was considered suitable for its purpose. Results from the experiment suggest the technical feasibility for the methodology and for the system, also adding knowledge about the interaction process of disabled people with a game.

Personalized gestural interaction applied in a gesture interactive game-based approach for people with disabilities

Due to the advancement in technology, new methods for human computer interaction are getting developed very rapidly. Interacting with computers or mobile devices through hand gestures is one of the important research area where researchers are trying to implement new user interfaces by applying various methods. The Hand Gesture Recognition system is useful to detect the gestures with the help of image capturing devices installed on computer and other gadgets. The rapid growth and improvement of mobile phones in past few years led to innovations in visualization and interaction with devices. Touchscreen gestures are widely used for interactivity in such devices but in near future the users may demand for more natural interaction methods. In this paper, the Accurate End Point Identification method is implemented and applied on gesture images which are captured in varying background and it is also applied on blurred images containing multiple objects. The AEPI method accurately recognizes the gestures from such images and provides a new dimension to implement user interface that will help to provide more natural inputs through hand gestures.

Using AEPI method for hand gesture recognition in varying background and blurred images

With the evolution of computing technologies the current user computer interaction devices like mouse, keyboard, joysticks, pen etc. are getting replaced by touch screen and hand gesture recognition based user interfaces etc. Hand Gesture Recognition Systems are gaining importance as they provide a new ways to extract meaningful information from the gesture. The meaningful information is utilized to solve various problems in virtual reality, playing games, perform analysis of sign language etc. When using hand gestures to develop new user computer interaction method it is necessary to identify the correct gesture from the hand gesture image. In this paper using morphological operations we prepare the hand gesture image which can be used to recognize the correct hand gesture. The method is implemented to detect the centroid of hand gesture by removing the unwanted objects, noise and black holes if any in a static true color image whose background is uniform.

Detecting centroid for hand gesture recognition using morphological computations

Hand gestures provide a natural and intuitive way to interact with the computers, mobile devices, etc. Many researchers are working on gesture recognition where the aim is to identify and distinguish human gestures and then identified gestures are used to control applications in specific domains. Recognizing real time hand gestures is very challenging and difficult task. This paper presents a novel Real Time End Point Identification Method from a real time video of hand gesture using computer vision and Image processing techniques. The Real Time End Point Identification (RTEPI) method discussed in [1] is based on Accurate End Point Identification [2], which enables the AEPI method to work on real time hand gestures captured through web camera or laptop camera. The RTEPI method provides the correct frame for real time processing to AEPI method which then detects the accurate method. The AEPI method has been implemented to address the problems of varying background, luminance, blurring etc. Five different phases of AEPI method includes preprocessing, centroid detection, removal of unwanted objects, thinning and recognition which are already discussed in [2,3]. The paper presents the result and performance analysis of RTEPI method for all possible input patterns of real time hand gesture recognition.

Performance analysis of RTEPI method for real time hand gesture recognition

Static Hand Gesture Recognition is an important area of research as it is used for developing a variety of useful applications in the domains like robotics, artificial intelligence, mobile games etc. Variety of useful methods is implemented to detect static hand gesture. In our previous study presented in [1] describes the implementation of Accurate End Point Identification (AEPI) Method for static hand gesture recognition. The AEPI method has been implemented to address the problems of varying background, luminance, blurring etc. Five different phases of AEPI method includes preprocessing, centroid detection, removal of unwanted objects, thinning and recognition which are already discussed in [1], [2]. In this paper, we present the result and performance analysis of AEPI method for all the possible input patterns of static hand gesture recognition.

A.V. Dehankar

Papers

Using AEPI method for hand gesture recognition in varying background and blurred images

Detecting centroid for hand gesture recognition using morphological computations

Performance analysis of RTEPI method for real time hand gesture recognition

Performance Analysis of Accurate End Point Identification Method of Static Hand Gesture Recognition

Controlling computer using eye movement