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

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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.
Funding Information
  • Jiangsu Overseas Visiting Scholar Program for University Prominent Young & Middle-Aged Teachers and Presidents of China (19KJA310002, 182102310629, 61602250, BK20180727, kx201901, RP202G0230, MC_PC_17171, RM60G0680)
  • Natural Science Foundation of Jiangsu Province (19KJA310002, 182102310629, 61602250, BK20180727, kx201901, RP202G0230, MC_PC_17171, RM60G0680)
  • Henan Key Research and Development Project (19KJA310002, 182102310629, 61602250, BK20180727, kx201901, RP202G0230, MC_PC_17171, RM60G0680)
  • National Natural Science Foundation of China (19KJA310002, 182102310629, 61602250, BK20180727, kx201901, RP202G0230, MC_PC_17171, RM60G0680)
  • Jiangsu Natural Youth Science Foundation (19KJA310002, 182102310629, 61602250, BK20180727, kx201901, RP202G0230, MC_PC_17171, RM60G0680)
  • Guangxi Key Laboratory of Trusted Software (19KJA310002, 182102310629, 61602250, BK20180727, kx201901, RP202G0230, MC_PC_17171, RM60G0680)
  • Royal Society (19KJA310002, 182102310629, 61602250, BK20180727, kx201901, RP202G0230, MC_PC_17171, RM60G0680)
  • Medical Research Council (19KJA310002, 182102310629, 61602250, BK20180727, kx201901, RP202G0230, MC_PC_17171, RM60G0680)
  • HOPE Foundation for Cancer Research (19KJA310002, 182102310629, 61602250, BK20180727, kx201901, RP202G0230, MC_PC_17171, RM60G0680)
  • British Heart Foundation (19KJA310002, 182102310629, 61602250, BK20180727, kx201901, RP202G0230, MC_PC_17171, RM60G0680)

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