Computer Vision Algorithms for Sign Language Recognition

Sign Language Recognition

Sign languages are complex, abstract linguistic systems, with their own grammars. This talk will introduce you to automated algorithms that can take sign language video and recognize the signs performed. This kind of ability would be useful in facilitating the communication between Deaf and hearing persons, mediated by a computing device coupled with cameras. The scientific goal is to ultimately go beyond the recognition of isolated signs or continuous signs in short sentences based on video, without the use of special equipment such as data gloves or magnetic markers. The talk will describe our experience with the design of scalable formalisms for representation, model learning, and matching methods, particularly those that can handle the errors in low-level methods.

Short Bio

Sudeep Sarkar

Sudeep Sarkar received his B.Tech degree in Electrical Engineering from the Indian Institute of Technology, Kanpur, in 1988, and his M.S. and Ph.D. degrees in Electrical Engineering from The Ohio State University, Columbus, in 1990 and 1993, respectively. Since 1993, he has been with the Computer Science and Engineering Department at the University of South Florida, Tampa, where he is currently a Professor. He is also a Research Administration Faculty Fellow in the university’s Office of Research and Innovation. His research interests include perceptual organization in single images and multiple image sequences, automated sign language recognition, biometrics, and nano-computing. He served on the editorial boards for the IEEE Transactions on Pattern Analysis and Machine Intelligence (1999-2003) and Pattern Analysis & Applications Journal during (2000-2001). He is currently serving on the editorial boards of the Pattern Recognition journal, IEEE Transactions on Systems, Man, and Cybernetics (Part-B), Image and Vision Computing, and IET Computer Vision. He is a Fellow of IAPR and the IEEE-CS Distinguished Visitor Program Speaker 2010-2012.