A Method for isolated sign recognition with KINECT

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2014
Işıklıgil, Emre
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Although there are various studies on sign language recognition (SLR), most of them use accessories like coloured gloves and accelerometers for data acquisition or require complex environmental setup to operate. In my thesis, I will use only Microsoft Kinect sensor for acquiring data for SLR. Kinect lets us obtain 3D positions of the body joints in real time without the help of any other device. After an isolated sign is captured, paths of the discriminative body joints are extracted. Then, a vector consisting of the extracted paths, called Sign Graph, is created to describe the isolated sign. To be able to compare two sign graphs, as the distance metric, I propose using the average warping distance of the joint paths that the sign graphs include. Dynamic Time Warping is used for effective calculation of the warping distance. Once a distance measure is defined between Sign Graphs, they are classified using k Nearest Neighbours algorithm. The proposed method performed better than the state of the art and achieved recognition rate of 59.3% in signer-independent experiments and 91.0% in signer-dependent experiments with a dataset consisting of 40 signs obtained from 13 different signers.
Pattern recognition systems., Sign language, Image processing, Computer graphics.
http://etd.lib.metu.edu.tr/upload/12617888/index.pdf
https://hdl.handle.net/11511/24013
Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats
E. Işıklıgil, “A Method for isolated sign recognition with KINECT,” M.S. - Master of Science, Middle East Technical University, 2014.
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