Accurate Facial Landmark Extraction

作者：Guo Jing Ming^*; Tseng Szu Han^*; Wong KokSheik^*

来源：IEEE Signal Processing Letters, 2016, 23(5): 605-609.

DOI：10.1109/LSP.2016.2543019

• Facial landmark extraction
• linear regression
• random forest

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.

• 出版日期2016-5