Expanding Training Set for Chinese Sign Language Recognition

In sign language recognition, one of the problems is to collect enough training data. Almost all of the statistical methods used in sign language recognition suffer from this problem. Inspired by the crossover of genetic algorithms, this paper presents a method to expand Chinese sign language (CSL) database through re-sampling from existing sign samples. Two original samples of the same sign are regarded as parents. They can reproduce their children by crossover. To verify the validity of the proposed method, some experiments are carried out on a vocabulary of 2435 gestures in Chinese sign language. Each gesture 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 test. The experimental results show that the new samples generated by the proposed method are effective