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Mobile Networks and Management. 10th EAI International Conference, MONAMI 2020, Chiba, Japan, November 10–12, 2020, Proceedings

Research Article

Bottleneck Feature Extraction-Based Deep Neural Network Model for Facial Emotion Recognition

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  • @INPROCEEDINGS{10.1007/978-3-030-64002-6_3,
        author={Tian Ma and Kavuma Benon and Bamweyana Arnold and Keping Yu and Yan Yang and Qiaozhi Hua and Zheng Wen and Anup Kumar Paul},
        title={Bottleneck Feature Extraction-Based Deep Neural Network Model for Facial Emotion Recognition},
        proceedings={Mobile Networks and Management. 10th EAI International Conference, MONAMI 2020, Chiba, Japan, November 10--12, 2020, Proceedings},
        proceedings_a={MONAMI},
        year={2020},
        month={12},
        keywords={Emotion recognition Deep neural network K-nearest neighbor Haar features},
        doi={10.1007/978-3-030-64002-6_3}
    }
    
  • Tian Ma
    Kavuma Benon
    Bamweyana Arnold
    Keping Yu
    Yan Yang
    Qiaozhi Hua
    Zheng Wen
    Anup Kumar Paul
    Year: 2020
    Bottleneck Feature Extraction-Based Deep Neural Network Model for Facial Emotion Recognition
    MONAMI
    Springer
    DOI: 10.1007/978-3-030-64002-6_3
Tian Ma1,*, Kavuma Benon1, Bamweyana Arnold1, Keping Yu2, Yan Yang1, Qiaozhi Hua3, Zheng Wen4, Anup Kumar Paul5
  • 1: College of Computer Science and Technology, Xi’an University of Science and Technology
  • 2: Global Information and Telecommunication Institute, Waseda University, Shinjuku
  • 3: Computer School, Hubei University of Arts and Science
  • 4: School of Fundamental Science and Engineering, Waseda University
  • 5: Department of Electronics and Communications Engineering, East West University
*Contact email: matian@xust.edu.cn

Abstract

Deep learning is one of the most effective and efficient methods for facial emotion recognition, but it still encounters stability and infinite feasibility problems for faces of different races. To address this issue, we proposed a novel bottleneck feature extraction (BFE) method based on the deep neural network (DNN) model for facial emotion recognition. First, we used the Haar cascade classifier with a randomly generated mask to extract the face and remove the background from the image. Second, we removed the last output layer of the VGG16 transfer learning model, which was applied only for bottleneck feature extraction. Third, we designed a DNN model with five dense layers for feature training and used the famous Cohn-Kanade dataset for model training. Finally, we compared the proposed model with the K-nearest neighbor and logistic regression models on the same dataset. The experimental results showed that our model was more stable and could achieve a higher accuracy and F-measure, up to 98.59%, than other methods.

Keywords
Emotion recognition Deep neural network K-nearest neighbor Haar features
Published
2020-12-22
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-64002-6_3
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