Re-sampling for Chinese Sign Language Recognition

In Sign Language recognition, one of the problems is to collect enough data. Data collection for both training and testing is a laborious but necessary step. Almost all of the statistical methods used in Sign Language Recognition suffer from this problem. Inspired by the crossover and mutation of genetic algorithms, this paper presents a method to enlarge Chinese Sign language database through re-sampling from existing sign samples. Two initial samples of the same sign are regarded as parents. They can reproduce their children by crossover. To verify the effectiveness of the proposed method, some experiments are carried out on a vocabulary with 350 static signs. Each sign has 4 samples. Three samples are used to be the original generation. These three original samples and their offspring are used to construct the training set, and the remaining sample is used for testing. The experimental results show that the data generated by the proposed method are effective.