Hand detection on sign language videos

Abstract

For gesture and sign language recognition, hand shape and hand motion are the primary sources of information that differentiate one sign from another. Building an efficient and reliable hand detector is therefore an important step in recognizing signs and gestures. In this paper we evaluate three hand detection methods on three sign language data sets: a skin and motion detector [1], hand detection using multiple proposals [12], and chains model [9].