Local binary patterns for multi-view facial expression recognition
Local binary patterns for multi-view facial expression recognition
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DOI：
10.1016/j.cviu.2010.12.001
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发表时间：
2011-04-01
影响因子：
4.5
通讯作者：
Bowden, R.
中科院分区：
文献类型：
--
作者：
Moore, S.;Bowden, R.
面部表情识别的研究主要应用于正面视图的人脸图像。一些尝试已经产生姿势不变的面部表情分类器。然而，大多数这些尝试只考虑偏航变化高达45度，在那里所有的脸都是可见的。很少有人研究不同姿势在面部表情识别中的内在潜力。这主要是由于可用的数据库通常只捕获正面视图的面部图像。最新的数据库BU3DFE和multi-pie允许对不同视角下的面部表情识别进行实证研究。采用连续两阶段的方法进行姿态分类和基于视角的面部表情分类，以研究从正面到侧面视图偏航变化的影响。研究了局部二值模式（lbp）及其作为纹理描述符的变化。这些特征允许研究方向和多分辨率分析对多视图面部表情识别的影响。研究了姿态对不同面部表情的影响。研究了其他因素，包括全局和局部特征向量的分辨率和构建。采用基于外观的方法，将图像划分为在面部上粗排列的子块。特征向量包含从每个子块构建的连接的特征直方图。采用多类支持向量机学习姿态和姿态依赖的面部表情分类器。(C) 2010爱思唯尔公司版权所有。
Research into facial expression recognition has predominantly been applied to face images at frontal view only. Some attempts have been made to produce pose invariant facial expression classifiers. However, most of these attempts have only considered yaw variations of up to 45 degrees, where all of the face is visible. Little work has been carried out to investigate the intrinsic potential of different poses for facial expression recognition. This is largely due to the databases available, which typically capture frontal view face images only. Recent databases, BU3DFE and multi-pie, allows empirical investigation of facial expression recognition for different viewing angles. A sequential 2 stage approach is taken for pose classification and view dependent facial expression classification to investigate the effects of yaw variations from frontal to profile views. Local binary patterns (LBPs) and variations of LBPs as texture descriptors are investigated. Such features allow investigation of the influence of orientation and multi-resolution analysis for multi-view facial expression recognition. The influence of pose on different facial expressions is investigated. Others factors are investigated including resolution and construction of global and local feature vectors. An appearance based approach is adopted by dividing images into sub-blocks coarsely aligned over the face. Feature vectors contain concatenated feature histograms built from each sub-block. Multi-class support vector machines are adopted to learn pose and pose dependent facial expression classifiers. (C) 2010 Elsevier Inc. All rights reserved.