Publication Type : Conference Paper
Publisher : 2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS)
Source : 2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 2013.
Url : http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=6745452
Keywords : Assistive technology, deaf community, dynamic gesture recognition, Dynamic Signs, Feature extraction, feature extraction method, Gesture recognition, global trajectory information, image motion analysis, Indian sign language, ISL, key frame extraction, key maximum curvature points, linguistic information, local hand motion, MCP, scale invariant, Shape, shape feature extraction, sign language recognition, spatial location, spatio temporal variability, Trajectory, translation invariant, Vectors, Videos, vision-based recognition, visual-spatial language
Campus : Amritapuri
School : Department of Computer Science and Engineering, School of Engineering
Department : Computer Science
Verified : Yes
Year : 2013
Abstract : Sign language is the most natural way of expression for the deaf community. Indian Sign Language (ISL) is a visual-spatial language which provides linguistic information using hands, arms, facial expressions, and head/body postures. In this paper we propose a new method for, vision-based recognition of dynamic signs corresponding to Indian Sign Language words. A new method is proposed for key frame extraction which is more accurate than the existing methods. The frames corresponding to the Maximum Curvature Points (MCPs) of the global trajectory are taken as the keyframes. The method accomodates the spatio temporal variability that may occur when different persons perform the same gesture. We are also proposing a new method based on spatial location of the Key Maximum Curvature Points of the boundary for shape feature extraction of key frames.Our method when compared with three other exisiting methods has given better performance. The method has considered the local as well as global trajectory information for recognition.The feature extraction method has proved to be scale invariant and translation invariant.
Cite this Research Publication :
M. Geetha and Aswathi, P. V., “Dynamic gesture recognition of Indian sign language considering local motion of hand using spatial location of Key Maximum Curvature Points”, in 2013 IEEE Recent Advances in Intelligent Computational Systems (RAICS), 2013.