Accurate facial landmark extraction

Jing Ming Guo, Szu Han Tseng, Koksheik Wong

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

Facial landmark extraction system is crucial in various applications, including face recognition, expression analysis, face tracking, and face animation. This letter aims to improve the performance of an existing landmark extraction method proposed by Ren et al. in terms of error rate. Specifically, the Gaussian blur filter is applied on the input image to reduce noise interference and the theta-based split rule is deployed to strengthen the performance of the random forests. Then, global linear regression is applied instead of treating each landmark independently. Experimental results demonstrate that the proposed modified facial landmark extraction algorithm outperforms the conventional methods for both the LFPW and Helen databases.

Original languageEnglish
Pages (from-to)605-609
Number of pages5
JournalIEEE Signal Processing Letters
Volume23
Issue number5
DOIs
Publication statusPublished - May 2016
Externally publishedYes

Keywords

  • Facial landmark extraction
  • linear regression
  • random forest

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