Smart City Development in China: One City One Policy

Release Date:2015-12-22 Author::Biyu Wan, Rong Ma, Weiru Zhou, and Guoqiang Zhang Click:

[Abstract] In this paper, we introduce a novel method for facial landmark detection. We localize facial landmarks according to the MAP criterion. Conventional gradient ascent algorithms get stuck at the local optimal solution. Gibbs sampling is a kind of Markov Chain Monte Carlo (MCMC) algorithm. We choose it for optimization because it is easy to implement and it guarantees global convergence. The posterior distribution is obtained by learning prior distribution and likelihood function. Prior distribution is assumed Gaussian. We use Principle Component Analysis (PCA) to reduce the dimensionality and learn the prior distribution. Local Linear Support Vector Machine (LL⁃SVM) is used to get the likelihood function of every key point. In our experiment, we compare our detector with some other well⁃known methods. The results show that the proposed method is very simple and efficient. It can avoid trapping in local optimal solution.

[Keywords] facial landmarks; MAP; Gibbs sampling; MCMC; LL⁃SVM