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Although the proposed non-complex networks did not provide the highest accuracy for this database (based on the literature), 1) they were able to provide better results than some of the more complex and even pre-trained networks in the literature, 2) they are trained very fast, and 3) they are quite appropriate and acceptable for the robotic system during Human-Robot Interactions (HRI) via sign language.Home Forza Motorsport 4 mods Forza Motorsport 4 X360 game - Mod D

The accuracy rate of 89.44% and 86.39% were obtained for the presented CNN-LSTM and 3D-CNN networks, respectively which were fairly promising for our automated lip-reading robotic system. In the second one, a 3D-CNN network was used to extract appropriate visual and temporal features from the videos. In the first network, CNN was used to extract static features, and LSTM was used to model temporal dynamics. In a follow up of our previous studies in empowering the RASA social robot to interact with individuals with hearing problems via sign language, we have proposed two automated lip-reading systems based on DNN architectures, a CNN-LSTM and a 3D-CNN, on the robotic system to recognize OuluVS2 database words. In Iranian Sign Language (ISL), alongside the movement of fingers/arms, the dynamic movement of lips is also essential to perform/recognize a sign completely and correctly. Book series (LNCS, volume 13086) Abstract

Automated lip reading software download