Fingerspelling recognition using synthetic images and deep transfer learning

Nam Nguyen Tu; Shinji Sako; Bogdan Kwolek

doi:10.1117/12.2587592

4 January 2021 Fingerspelling recognition using synthetic images and deep transfer learning

Nam Nguyen Tu, Shinji Sako, Bogdan Kwolek

Proceedings Volume 11605, Thirteenth International Conference on Machine Vision; 116051U (2021) https://doi.org/10.1117/12.2587592
Event: Thirteenth International Conference on Machine Vision, 2020, Rome, Italy

Abstract

Although gesture recognition has been intensely studied for decades, it is still a challenging research topic due to difficulties posed by background complexity, occlusion, viewpoint, lighting changes, the deformable and articulated nature of hands, etc. Numerous studies have shown that extending the training dataset with real images about synthetic images improves the recognition accuracy. However, little work is devoted to demonstrate what improvements in recognition can be achieved thanks to transferring the style onto synthetically generated images from the real gestures. In this paper, we propose a novel method for Japanese fingerspelling recognition using both real and synthetic images generated on the basis of a 3D hand model. We propose to employ a neural style transfer to include information from real images onto synthetically generated dataset. We demonstrate experimentally that neural style transfer and discriminative layer training applied to training deep neural models allow obtaining considerable gains in the recognition accuracy.

Citation Download Citation

Nam Nguyen Tu, Shinji Sako, and Bogdan Kwolek "Fingerspelling recognition using synthetic images and deep transfer learning", Proc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116051U (4 January 2021); https://doi.org/10.1117/12.2587592

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