Facial expression recognition using bag of distances

Fu Song Hsu, Wei Yang Lin*, Tzu Wei Tsai

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


The automatic recognition of facial expressions is critical to applications that are required to recognize human emotions, such as multimodal user interfaces. A novel framework for recognizing facial expressions is presented in this paper. First, distance-based features are introduced and are integrated to yield an improved discriminative power. Second, a bag of distances model is applied to comprehend training images and to construct codebooks automatically. Third, the combined distance-based features are transformed into mid-level features using the trained codebooks. Finally, a support vector machine (SVM) classifier for recognizing facial expressions can be trained. The results of this study show that the proposed approach outperforms the state-of-the-art methods regarding the recognition rate, using a CK+ dataset.

Original languageEnglish
Pages (from-to)309-326
Number of pages18
JournalMultimedia Tools and Applications
Issue number1
StatePublished - 17 Sep 2014


  • Bag of distances
  • Facial expression recognition
  • Facial features


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