Facial Landmark Localization by Gibbs Sampling

作     者：Bofei Wang Diankai Zhang Chi Zhang Jiani Hu Weihong Deng 

作者机构：ZTE Corporation Beijing University of Posts and Telecommunication 

出 版 物：《ZTE Communications》 (中兴通讯技术（英文版）)

年 卷 期：2014年第12卷第4期

页      面：23-29页

学科分类：0810[工学-信息与通信工程] 08[工学] 080203[工学-机械设计及理论] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0835[工学-软件工程] 0802[工学-机械工程] 081002[工学-信号与信息处理] 

基　　金：supported by ZTE Industry-Academia-Research Cooperation Funds 

主　　题：facial landmarks MAP Gibbs sampling MCMC LL-SVM 

摘      要：In this paper, we introduce a novel method for facial landmark detection. We localize facial landmarks according to the MAP crite rion. 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 conver gence. 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 （LLSVM） is used to get the likelihood function of every key point. In our experiment, we compare our de tector with some other wellknown methods. The results show that the proposed method is very simple and efficient. It can avoid trapping in local optimal solution.