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In Automatic Sign Language Recognition (ASLR), robust hand tracking and detection is key to good recognition accuracy. We introduce a new dataset of depth data from continuously signed American Sign Language (ASL) sentences. We present analysis showing numerous errors of the Microsoft Kinect Skeleton Tracker (MKST) in cases where hands are close to the body, close to each other, or when the arms cross...
Low-cost depth cameras create new opportunities for robust and ubiquitous vision-based interfaces. While much research has focused on full-body pose estimation and the interpretation of gross body movement, this work investigates skeleton-free hand detection, tracking, and shape classification. Our goal is to build a rich and reliable gestural interface by developing methods that recognize a broad...
The CopyCat game is an interactive educational adventure game to help deaf children improve their language and memory abilities. As part of the CopyCat project, several computer-assisted language learning games have been designed, one of the games “Alien” is shown in Figure 1. Each game entails some sort of quest by the hero to collect items in order to solve a problem. In each quest, the child interacts...
We perform real-time American Sign Language (ASL) phrase verification for an educational game, CopyCat, which is designed to improve deaf children's signing skills. Taking advantage of context information in the game we verify a phrase, using Hidden Markov Models (HMMs), by applying a rejection threshold on the probability of the observed sequence for each sign in the phrase. We tested this approach...
We propose a novel approach for American Sign Langauge (ASL) phrase verification that combines confidence measures (CM) obtained from aligning forward sign models (the conventional approach) to the input data with the CM's obtained from aligning reversed sign models to the same input. To demonstrate our approach we have used two CM's, the Normalized likelihood score and the Log-Likelihood Ratio (LLR)...
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