Improving signer-independent sign language recognition

There is a huge gap between signer-independent sign language recognition and signer-dependent sign language recognition systems. The data variance from different signers in sign language makes it difficult to extract effective common features of data in signer-independent recognition. This data variance unavoidably affects the effect of signer-independent sign language recognition. This paper presents a model of an allowable variance range. It uses the learning and reasoning abilities of manifold concept to deal with sign language data variance. An easy and intuitive derivation method was created to establish the extremum of the function. A manifold tangent vectors based sign language recognition statistical model (TV/HMM) was applied in our signer-independent sign language recognition to resolve data variance in signer-independent sign language recognition. Experiments showed that, compared with traditional HMM recognition systems, the average discrimination rate significantly improves.