Automatic 3D Facial Expression Recognition Based on a Bayesian Belief Net and a Statistical Facial Feature Model
Automatic 3D Facial Expression Recognition Based on a Bayesian Belief Net and a Statistical Facial Feature Model
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DOI：
10.1109/icpr.2010.907
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发表时间：
2010-08
期刊：
影响因子：
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通讯作者：
Xi Zhao;Di Huang;E. Dellandréa;Liming Chen
中科院分区：
文献类型：
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作者：
Xi Zhao;Di Huang;E. Dellandréa;Liming Chen
基于3D人脸数据的人脸表情自动识别仍然是一个具有挑战性的问题。本文提出了一种将贝叶斯信任网(BBN)和统计人脸特征模型(SfAM)相结合的表情识别方法。利用本文提出的参数计算方法，针对具体问题设计了一种新的边界层神经网络。通过学习人脸地标形状(形态)的全局变化和地标周围纹理和形状的局部变化，可变形统计面部特征模型(SfAM)不仅可以执行自动地标，还可以计算用于反馈BBN的置信度。在公共3D人脸表情数据库BU-3DFE上进行了测试，该方法能够成功地识别表情，平均识别率达到82%以上。
Automatic facial expression recognition on 3D face data is still a challenging problem. In this paper we propose a novel approach to perform expression recognition automatically and flexibly by combining a Bayesian Belief Net (BBN) and Statistical facial feature models (SFAM). A novel BBN is designed for the specific problem with our proposed parameter computing method. By learning global variations in face landmark configuration (morphology) and local ones in terms of texture and shape around landmarks, morphable Statistic Facial feature Model (SFAM) allows not only to perform an automatic landmarking but also to compute the belief to feed the BBN. Tested on the public 3D face expression database BU-3DFE, our automatic approach allows to recognize expressions successfully, reaching an average recognition rate over 82%.