Sign Language Recognition Based on 3D Convolutional Neural Networks

Abstract

The inclusion of disabled people is still a recurring problem throughout the world. For the hearing impaired, the barrier imposed by the sign language spoken by a small part of the population imposes limitations that interfere in the quality of life of these people. The popularization or even automation of sign language recognition can take their lives to a higher level. Understanding the importance of sign language recognition for the hearing impaired we propose a 3D CNN architecture for the recognition of 64 classes of gestures from Argentinian Sign Language (LSA64). We demonstrate the efficiency of the method when compared to traditional methods based on hand-crafted features and that its results outperform most deep learning-based work reaching 93.9% of accuracy.