A Similarity Measurement Method of Facial Expression Based on Geometric Features
HUANG Zhong1,2, HU Min1, WANG Xiao-Hua1
1.Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine, School of Computer and Information, Hefei University of Technology, Hefei 230009 2.School of Physics and Electric Engineering, Anqing Normal University, Anqing 246011
Abstract In facial animations such as performance-driven and expression cloning, it needs to find the most similar expression to enhance the reality and fidelity of animations. A feature-weighted expression similarity measurement method is proposed based on facial geometric features. Firstly, chain code is used to characterize shape features for local expression regions, meanwhile deformation features are built based on topological relations among regional feature points to reflect holistic expression information. Then, feature-weighted method is adopted to measure the similarities of fused geometric features, and the solving process of feature weights is transformed to minimizing process of the weighted objective function. Finally, the solved weights as well as feature weighting functions are performed to measure similarities between two expressions and seek the most similar image with a input expression image. The experimental results on BU-3DFE database and FEEDTUM database show that the proposed method has significantly higher accuracy in seeking similar expressions than existing measurement methods, and it keeps better robustness for the expressions with different categories and different intensities, especially in local details such as the shape of mouth, the contraction of cheek, and the open-close amplitude of mouth.
2015, Vol. 28 
