A Customized Convolutional Neural Network Design Using Improved Softmax Layer for Real-time Human Emotion Recognition

Kai Yen Wang, Yu De Huang, Yun Lung Ho, Wai Chi Fang*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

This paper proposes an improved softmax layer algorithm and hardware implementation, which is applicable to an effective convolutional neural network of EEG-based real-time human emotion recognition. Compared with the general softmax layer, this hardware design adds threshold layers to accelerate the training speed and replace the Euler's base value with a dynamic base value to improve the network accuracy. This work also shows a hardware-friendly way to implement batch normalization layer on chip. Using the EEG emotion DEAP[7] database, the maximum and mean classification accuracy were achieved as 96.03% and 83.88% respectively. In this work, the usage of improved softmax layer can save up to 15% of training model convergence time and also increase by 3 to 5% the average accuracy.

Original languageEnglish
Title of host publicationProceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages102-106
Number of pages5
ISBN (Electronic)9781538678848
DOIs
StatePublished - Mar 2019
Event1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 - Hsinchu, Taiwan
Duration: 18 Mar 201920 Mar 2019

Publication series

NameProceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019

Conference

Conference1st IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019
CountryTaiwan
CityHsinchu
Period18/03/1920/03/19

Keywords

  • Batch Normalization Layer
  • Convolutional Neural Network
  • Deep Learning
  • Hardware Machine Learning
  • Improved Softmax Layer
  • Threshold Layer

Fingerprint Dive into the research topics of 'A Customized Convolutional Neural Network Design Using Improved Softmax Layer for Real-time Human Emotion Recognition'. Together they form a unique fingerprint.

  • Cite this

    Wang, K. Y., Huang, Y. D., Ho, Y. L., & Fang, W. C. (2019). A Customized Convolutional Neural Network Design Using Improved Softmax Layer for Real-time Human Emotion Recognition. In Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019 (pp. 102-106). [8771616] (Proceedings 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems, AICAS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AICAS.2019.8771616