Earticle

초록

영어

In this research, we have explored state-of-the-art deep learning algorithms for face emotion recognition. In this regard, we utilize a transfer learning technique for recognizing seven classes of emotions using image classifiers such as MobileNetv2, GoogleNet, ResNet101, VGGNet19. In addition, we also compared our results with a specially designed deep learning model for face emotion recognition such as “Deep Emotion”. We have utilized the dataset FER2013 exploited by many researchers. We have analyzed the accuracy, time of training, complexity in terms of layers, and other parameters. After training on FER2013, we have deployed each model on a Korean dataset obtained from Korean dramas videos containing images of celebrities. Our ultimate goal was to label all unknown Korean dataset after training on FER2013. We have presented three different programs, i.e. (i) automatic labeling, (ii) transfer learning-based models for unknown images, (iii) deep-emotion model for face emotion recognition. We have presented a detailed analysis of all models and our corresponding programs.

목차

Abstract
1. Introduction
2. Related Works
3. Methodology
3.1. Transfer Learning
3.2. Automatic Labelling for Korean Datasets
4. Experiments
4.1. Experimental setup
4.2. Experimental result
5. Conclusions
Acknowledgment
References

키워드

저자

• 울라 샨 [ Shan Ullah | Department of Electrical and Computer Engineering, Inha University, Incheon, South Korea ]
• 김덕환 [ Deok-Hwan Kim | Department of Electrical and Computer Engineering, Inha University, Incheon, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004