Sign language recognition of invariant features based on multiclass support vector machine with beam ECOC optimization

This paper presents a recognition system for understanding the American Sign Language using Support Vector Machine (SVM) and Error Correcting Output Codes (ECOC). In the pre-processing stage, invariant local features were extracted to develop the recognition system using multi-class support vector machine. The binary classifier of SVM employed with ECOC framework to deal multi-class problems. In ECOC, the encoding matrix was designed using various coding strategy such as one versus one, one versus all and random matrix. The data has trained based on coding matrix with set of dichotomizes independently and classify the data using loss-based decoding method to obtain efficient accuracy. In this paper, performance of recognition rate has been improved by updating given ECOC matrix from the original matrix using beam ECOC optimization. By employing beam ECOC, classification accuracy was increased from 70 to 80% than the state-of-art coding strategy.