Abstract
In this paper, we employ a zero-order local deformation model to model the visual variability of video streams of American sign language (ASL) words. We discuss two possible ways of combining the model with the tangent distance used to compensate for affine global transformations. The integration of the deformation model into our recognition system improves the error rate on a database of ASL words from 22.2% to 17.2%.
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Zahedi, M., Keysers, D., Deselaers, T., Ney, H. (2005). Combination of Tangent Distance and an Image Distortion Model for Appearance-Based Sign Language Recognition. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds) Pattern Recognition. DAGM 2005. Lecture Notes in Computer Science, vol 3663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550518_50
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DOI: https://doi.org/10.1007/11550518_50
Publisher Name: Springer, Berlin, Heidelberg
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