Synthetic data generation technique in Signer-independent sign language recognition

The lack of training samples is an important problem in the field of sign language recognition. This paper presents a method of generating synthetic multi-stream samples so as to enlarge the training set of sign. The mean shift algorithm is able to obtain the directions of maximum increase and decrease in the density function, so it is used to control the direction and the intensity of synthetic data generation. The synthetic data generation proposed in this paper satisfies the need of the synthetic samples, which must include a large amount of effective information of unspecific signers. The proposed method is evaluated under different experimental conditions, such as the generating strategy, the capacity of the model, as well as the intensity and direction of the generating process. The results show that in most cases recognition accuracy is improved; and in some, even greatly improved.