In this work we extend WGAN-GP in order to achieve better generation of synthesized images for finger spelling classification. The main difference between the ordinary WGAN-GP and the proposed algorithm is that in the training we employ both training samples and training labels. These training labels are fed to the generator, that generates the synthetic images using both the randomized latent input and the input label. In ordinary WGAN-GP, latent input variables are usually sampled from an unconditional prior. In the proposed algorithm the latent input vector is a concatenation of random part, the class labels and additional variables that are drawn from Gaussian distributions representing hand poses or gesture attributes. The JSL dataset for Hiragana sign recognition has been balanced using the rendered samples on the basis of a 3D hand model as well as the extended WGAN-GP.

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110411U (15 March 2019); doi: 10.1117/12.2522935

Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)