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Local Binary Pattern based features for Sign Language Recognition
| Content Provider | CiteSeerX |
|---|---|
| Abstract | Abstract. In this paper we focus on appearance features particularly the Local Binary Patterns describing the manual component of Sign Language. We com-pare the performance of these features with geometric moments describing the trajectory and shape of hands. Since the non-manual component is also very im-portant for sign recognition we localize facial landmarks via Active Shape Model combined with Landmark detector that increases the robustness of model fitting. We test the recognition performance of individual features and their combina-tions on a database consisting of 11 signers and 23 signs with several repetitions. Local Binary Patterns outperform the geometric moments. When the features are combined we achieve a recognition rate up to 99.75 % for signer dependent tests and 57.54 % for signer independent tests. 1 |
| File Format | |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
