Precise Facial Feature Localization under Non-Restraint Environment with Limited Training Images
CHEN Ying1,2,ZHANG Long-Yuan1
1. Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education,School of Internet of Things Engineering,Jiangnan University,Wuxi 214122 2.Key Laboratory of System Control and Information Processing of Ministry of Education,Shanghai Jiao Tong University,Shanghai 200240
Abstract：After analyzing the limitation of current methods,a precise localization strategy with limited training data is proposed in a probability framework. Texture and geometry information of facial elements are extracted as model features after comparison analysis with other traditional descriptors. Gaussian mixture model is used for the probability modeling,which describes the distribution of each model features extracted from different facial conditions well. Then,a series of fusion strategies are designed for the facial features localization,which considers the probability distribution of each facial feature,the distribution characters of their surrounding elements and their geometry constraints. The experimental results show that the proposed method can realize precise localization for the facial features with limited training sample images which belong to a single subject,and it outperforms other methods in localization accuracy.
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2013, Vol. 26 
