Abstract
The simultaneous-sequential nature of sign language production, which employs hand gestures and body motions combined with facial expressions, still challenges sign language recognition algorithms. This paper presents a method to recognize Brazilian Sign Language (Libras) using Kinect. Skeleton information is used to segment sign gestures from a continuous stream, while depth information is used to provide distinctive features. The method was assessed in a new data-set of 107 medical signs selected from common dialogues in health-care centers. The dynamic time warping–nearest neighbor (DTW-kNN) classifier using the leave-one-out cross-validation strategy reported outstanding results.
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CITATION STYLE
Vidalón, J. E. Y., & De Martino, J. M. (2016). Brazilian sign language recognition using kinect. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9914 LNCS, pp. 391–402). Springer Verlag. https://doi.org/10.1007/978-3-319-48881-3_27
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