Journal of Electrical Engineering and Technology

  • 제13권1호
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  • Pages.485-492
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  • 2018
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  • 1975-0102(pISSN)
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  • 2093-7423(eISSN)
대한전기학회 (The Korean Institute of Electrical Engineers)
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Facial Expression Classification Using Deep Convolutional Neural Network

  • Choi, In-kyu (Dept. of Electronic Engineering, Kwangwoon Univerity) ;
  • Ahn, Ha-eun (Dept. of Electronic Engineering, Kwangwoon Univerity) ;
  • Yoo, Jisang (Dept. of Electronic Engineering, Kwangwoon Univerity)
  • 투고 : 2017.05.01
  • 심사 : 2017.10.24
  • 발행 : 2018.01.01
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초록

In this paper, we propose facial expression recognition using CNN (Convolutional Neural Network), one of the deep learning technologies. The proposed structure has general classification performance for any environment or subject. For this purpose, we collect a variety of databases and organize the database into six expression classes such as 'expressionless', 'happy', 'sad', 'angry', 'surprised' and 'disgusted'. Pre-processing and data augmentation techniques are applied to improve training efficiency and classification performance. In the existing CNN structure, the optimal structure that best expresses the features of six facial expressions is found by adjusting the number of feature maps of the convolutional layer and the number of nodes of fully-connected layer. The experimental results show good classification performance compared to the state-of-the-arts in experiments of the cross validation and the cross database. Also, compared to other conventional models, it is confirmed that the proposed structure is superior in classification performance with less execution time.

키워드

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Fig. 1. Examples of images classified as incorrect faces inFER 2013 database

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Fig. 2. The result of converting cut-out face region imageinto gray image

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Fig. 3. The result of applying data augmentation technique

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Fig. 4. The proposed CNN architecture

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Fig. 5. Computational relationship between consecutiveconvolutional layers

Table 1. Accuracy comparison of data augmentation techniques

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Table 2. Average Top 1 Accuracy(%) on cross validation

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Table 3. Average confusion matrix on cross validation (%)

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Table 4. Average Accuracy(%) on cross database

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Table 5. Training and testing time for each model (batch : 128)

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Table 6. Accuracy(%) for each model

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Table 7. Confusion matrix on ADFES (%)

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Table 8. Confusion matrix on CFD (%)

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Table 9. Confusion matrix on CK+ (%)

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Table 10. Confusion matrix on EU-Emotion Stimulus Set (%)

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Table 11. Confusion matrix on ESRC (%)

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Table 12. Confusion matrix on FACE DATABASE (%)

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Table 13. Confusion matrix on KDEF (%)

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Table 14. Confusion matrix on RafD (%)

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Table 15. Confusion matrix on Web Search (%)

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Table 16. Confusion matrix on WSEFEP (%)

E1EEFQ_2018_v13n1_485_t0016.png 이미지

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