Smile detection in the wild with hierarchical visual feature

Jiahuiran Li, Junkai Chen, Zheru Chi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

9 Citations (Scopus)


Smile detection in the wild is an interesting and challenging problem. This paper presents an efficient approach with hierarchical visual feature to handle this problem. In our approach, Gabor filters with multi-scale, multi-orientation are first applied to extract facial textures namely Gabor faces from the input face image. After this, Histograms of Oriented Gradients (HOG) are employed to encode these extracted Gabor faces to capture and characterize the facial appearance characteristics. We further adopt a pooling strategy to transform the multiple HOG features into a global visual feature called Gabor-Hog. Finally, SVM is trained to perform the classification. The experiments conducted on the GENKI4K database show that the proposed visual feature is robust to distinguish a smile face from a no-smile face. Our method also achieves a promising performance compared with the other state-of-the-art methods.
Original languageEnglish
Title of host publication2016 IEEE International Conference on Image Processing, ICIP 2016 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781467399616
Publication statusPublished - 3 Aug 2016
Event23rd IEEE International Conference on Image Processing, ICIP 2016 - Phoenix Convention Center, Phoenix, United States
Duration: 25 Sep 201628 Sep 2016


Conference23rd IEEE International Conference on Image Processing, ICIP 2016
Country/TerritoryUnited States


  • Gabor filters
  • Gabor-HOG
  • HOG
  • Smile detection

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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