Hand-crafted feature guided deep learning for facial expression recognition

Guohang Zeng, Jiancan Zhou, Xi Jia, Weicheng Xie, Linlin Shen

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

35 Citations (Scopus)

Abstract

A number of facial expression recognition algorithms based on hand-crafted features and deep neutral networks have been developed. Motivated by the similarity between the hand-crafted features and features learned by deep network, a new feature loss is proposed to embed the information of hand-crafted features into the training process of network, which tries to reduce the difference between the two features. Based on the feature loss, a general framework for embedding the traditional feature information was developed and tested using CK+, JAFFE and FER2013 datasets. Experimental results show that the proposed network achieves much better accuracy than the original hand-crafted feature and the network without using our feature loss. When compared with other algorithms in literature, our network also achieved the best performance on CK+ dataset, i.e. 97.35% accuracy has been achieved.

Original languageEnglish
Title of host publicationProceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages423-430
Number of pages8
ISBN (Electronic)9781538623350
DOIs
Publication statusPublished - 5 Jun 2018
Externally publishedYes
Event13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018 - Xi'an, China
Duration: 15 May 201819 May 2018

Publication series

NameProceedings - 13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018

Conference

Conference13th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2018
Country/TerritoryChina
CityXi'an
Period15/05/1819/05/18

Keywords

  • Deep metric learning
  • Facial expression recognition
  • Feature loss
  • Hand crafted feature

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Control and Optimization

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