This paper presents an approach for recognizing signs made by hearing impaired people at sentence level. The signs are captured in the form of video and each frame is processed to efficiently extract sign information to model the sign and recognize instances of new test signs. Low-dimensional global "gist" descriptors are used to capture sign information from every frame of a sign video. K-means clustering is used to choose fixed number of frames, which are discriminative enough to distinguish between signs. Also, selection of fixed number of frames helps us to deal with unequal number of frames among the instances of same sign due to different signers and reduce the complexity of subsequent processing. Further, we exploit the concept of symbolic data analysis to effectively represent a sign. A fuzzy trapezoidal membership function is used to establish the similarity between test and a reference sign and a nearest neighbour classification technique is used to recognize the given test sign. A considerably large database of signs UoM-ISL is created and an extensive experimentation is conducted on this database to study the efficacy of the proposed methodology. The experimental results are found to be encouraging.
['H. S. Nagendraswamy', 'B.M. Chethana Kumara', 'R. Lekha Chinmayi']
GIST Descriptors for Sign Language Recognition: An Approach Based on Symbolic Representation