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The sign language recognition is the most popular research area involving computer vision, pattern recognition and image processing. It enhances communication capabilities of the mute person. In this paper, we present an object based key frame selection., Hausdorff distance, Forward Algorithm and Euclidean distance are used for shape similarity for hand gesture recognition. We proposed use to the...
We investigate the issue of sign language automatic phonetic subunit modeling, that is completely data driven and without any prior phonetic information. A first step of visual processing leads to simple and effective region-based visual features. Prior to the sub-unit modeling we propose to employ a pronunciation clustering step with respect to each sign. Afterwards, for each sign and pronunciation...
We present a novel and robust system for recognizing two handed motion based gestures performed within continuous sequences of sign language. While recognition of valid sign sequences is an important task in the overall goal of machine recognition of sign language, detection of movement epenthesis is important in the task of continuous recognition of natural sign language. We propose a framework for...
Facial expressions play a vital role in sign language by changing the meaning of the performed sign. In this work, we propose a system composed of a facial feature tracker based on active shape models and a classifier based on hidden Markov models to recognize common facial expressions used in sign language. Face tracker works in multi-resolution and multi-view to track faces in different poses fast...
Principal component analysis (PCA) and multiple discriminant analysis (MDA) have long been used for appearance-based hand posture recognition. In this paper, we propose a novel PCA/MDA scheme for hand posture recognition. The scheme is represented by two layers of nodes (classes). The first layer of nodes acts as a crude classification using PCA, and each input pattern is given a likelihood of being...
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