Author/Authors :
Jiang, Xianwei Nanjing Normal University of Special Education, China , Hu, Bo Nanjing Normal University of Special Education, China , Satapathy, Suresh Chandra School of Computer Engineering - KIIT Deemed to University, Bhubaneswar, India , Wang, Shui-Hua School of Computer Science and Technology - Henan Polytechnic University, Jiaozuo, China , Zhang, Yu-Dong School of Informatics - University of Leicester, Leicester LE1 7RH, UK
Abstract :
As an important component of universal sign language and the basis of other sign language learning, finger sign language is of great significance. This paper proposed a novel fingerspelling identification method for Chinese Sign Language via AlexNet-based transfer learning and Adam optimizer, which tested four different configurations of transfer learning. Besides, in the experiment, Adam algorithm was compared with stochastic gradient descent with momentum (SGDM) and root mean square propagation (RMSProp) algorithms, and comparison of using data augmentation (DA) against not using DA was executed to pursue higher performance. Finally, the best accuracy of 91.48% and average accuracy of 89.48 ± 1.16% were yielded by configuration M1 (replacing the last FCL8) with Adam algorithm and using 181x DA, which indicates that our method can identify Chinese finger sign language effectively and stably. Meanwhile, the proposed method is superior to other five state-of-the-art approaches.
Keywords :
Fingerspelling , Identification , Chinese Sign Language , Transfer Learning , via AlexNet-Based , Adam Optimizer
