Sparse deep feature learning for facial expression recognition

Weicheng Xie, Xi Jia, Linlin Shen, Meng Yang

Research output: Journal PublicationArticlepeer-review

41 Citations (Scopus)


While weight sparseness-based regularization has been used to learn better deep features for image recognition problems, it introduced a large number of variables for optimization and can easily converge to a local optimum. The L2-norm regularization proposed for face recognition reduces the impact of the noisy information, while expression information is also suppressed during the regularization. A feature sparseness-based regularization that learns deep features with better generalization capability is proposed in this paper. The regularization is integrated into the loss function and optimized with a deep metric learning framework. Through a toy example, it is showed that a simple network with the proposed sparseness outperforms the one with the L2-norm regularization. Furthermore, the proposed approach achieved competitive performances on four publicly available datasets, i.e., FER2013, CK+, Oulu-CASIA and MMI. The state-of-the-art cross-database performances also justify the generalization capability of the proposed approach.

Original languageEnglish
Article number106966
JournalPattern Recognition
Publication statusPublished - Dec 2019
Externally publishedYes


  • Deep metric learning
  • Expression recognition
  • Feature sparseness
  • Fine tuning
  • Generalization capability

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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