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Classifying Non-manual Markers in American Sign Language

Facial expressions and other non-manual markers are the primary means by which American Sign Language (ASL) users signal sentence and clause type, negation, and a variety of adverbs. Work in the area of non-manual marker recognition has the potential to make ASL-toEnglish translation available in contexts where interpreters and human relay services are currently rare, such as events with non-signing family members. It also promises to generate scientific insights into signed languages, an area of linguistics of which we know relatively little. Despite the potential for work in the area of signed language recognition, there is only limited research investigating how to automatically identify non-manual markers from video streams. We contribute to the field by evaluating the performance of three feature extraction methods at the binary classification problem of identifying the presence of a wh-question in streaming RGB video. Unlike previous work that attempts to classify utterance types as units, we focus on feature selection for identifying the presence/absence of a non-manual marker at different times in an utterance, in order to lay a foundation for machine translation work. With theory-based feature tracking, we achieve approximately 80% frame-level accuracy and 76% accuracy on sequences. Despite limited data, our attempt to use abstract features through neural networks resulted in close to 72% frame-level accuracy and 80% accuracy on sequences. A third approach using the ScaleInvariant Feature Transform algorithm was unimpressive, with 59% accuracy.