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Weakly supervised facial expression recognition via transferred DAL-CNN and active incremental learning

  • Ying Xu
  • Jian Liu
  • Yikui ZhaiEmail author
  • Junying Gan
  • Junying Zeng
  • He Cao
  • Fabio Scotti
  • Vincenzo Piuri
  • Ruggero Donida Labati
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Abstract

In recent years, facial expression recognition (FER) has becoming a growing topic in computer vision with promising applications on virtual reality and human–robot interaction. Due to the influence of illumination, individual differences, attitude variation, etc., facial expression recognition with robust accuracy in complex environment is still an unsolved problem. Meanwhile, with the wide use of social communication, massive data are uploaded to the Internet; the effective utilization of those data is still a challenge due to noisy label phenomenon in the study of FER. To resolve the above-mentioned problems, firstly, a double active layer-based CNN is established to recognize the facial expression with high accuracy by learning robust and discriminative features from the data, which could enhance the robustness of network. Secondly, an active incremental learning method was utilized to tackle the problem of using Internet data. During the training phase, a two-stage transfer learning method is explored to transfer the relative information from face recognition to FER task to alleviate the inadequate training data in deep convolution network. Besides, in order to make better use of facial expression data from Web site and further improve the FER accuracy, Unconstrained Facial Expression Database from Web site database is built in this paper. Extensive experiments performed on two public facial expression recognition databases FER 2013 and SFEW 2.0 have demonstrated that the proposed scheme outperforms the state-of-the-art methods, which could achieve 67.08% and 51.90%, respectively.

Keywords

Deep convolutional neural network Facial expression recognition Two-stage transfer learning Weakly supervised active incremental learning 

Notes

Acknowledgements

This work is supported by National Natural Science Foundation of China (No. 61771347); Characteristic Innovation Project of Guangdong Province (No. 2017KTSCX181); Young Innovative Talents Project of Guangdong Province (2017KQNCX206); Jiangmen Science and Technology Project ([2017] No. 268); 2017 Guangdong Science and Technology Plan Project (No. 2017A010101019); Youth Foundation of Wuyi University (No. 2015zk11); the Opening Project of GuangDong Province Key Laboratory of Information Security Technology (Grant No. 2017B030314131); the 2018 Opening Project of GuangDong Province Key Laboratory of Digital Signal and Image Processing.

Compliance with ethical standards

Conflict of interest

All authors declare that he/she has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Intelligent ManufacturingWuyi UniversityJiangmenChina
  2. 2.Dipartimento di InformaticaUniversita’ Degli Studi di MilanoMilanItaly

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