Fingerspelling Identification for Chinese Sign Language via AlexNet-Based Transfer Learning and Adam Optimizer

Xianwei Jiang, Bo Hu, Suresh Chandra Satapathy, Shui Hua Wang*, Yu Dong Zhang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

56 Citations (Scopus)

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.

Original languageEnglish
Article number3291426
JournalScientific Programming
Volume2020
DOIs
Publication statusPublished - 2020
Externally publishedYes

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