Multi-View Facial Expression Recognition Based on Group Sparse Reduced-Rank Regression
Multi-View Facial Expression Recognition Based on Group Sparse Reduced-Rank Regression
复制标题
DOI：
10.1109/taffc.2014.2304712
复制
发表时间：
2014-02
影响因子：
11.2
通讯作者：
Wenming Zheng
中科院分区：
文献类型：
--
作者：
Wenming Zheng
提出了一种新的多视角人脸表情识别方法。与大多数表情识别方法中使用单视图的人脸特征向量不同，本文综合多视图的人脸特征向量，并通过联合收割机将它们进行组合来实现表情识别。在人脸特征提取中，我们使用多尺度网格将人脸图像划分为一组子区域，并在每个子区域内进行特征提取。为了处理表情的预测，我们提出了一种新的组稀疏降秩回归（GSRRR）模型来描述多视图人脸特征向量和相应的表情类标签向量之间的关系。GSRRR的组稀疏性使我们能够自动选择对表情识别贡献最大的最佳子区域。为了解决GSRRR的优化问题，我们提出了一个有效的算法，使用不精确增广拉格朗日乘子（ALM）方法。最后，我们在BU-3DFE和Multi-PIE人脸表情数据库上进行了大量的实验，以评估所提出的方法的识别性能。实验结果证实了更好的识别性能相比，最先进的方法所提出的方法。
In this paper, a novel multi-view facial expression recognition method is presented. Different from most of the facial expression methods that use one view of facial feature vectors in the expression recognition, we synthesize multi-view facial feature vectors and combine them to this goal. In the facial feature extraction, we use the grids with multi-scale sizes to partition each facial image into a set of sub regions and carry out the feature extraction in each sub region. To deal with the prediction of expressions, we propose a novel group sparse reduced-rank regression (GSRRR) model to describe the relationship between the multi-view facial feature vectors and the corresponding expression class label vectors. The group sparsity of GSRRR enables us to automatically select the optimal sub regions of a face that contribute most to the expression recognition. To solve the optimization problem of GSRRR, we propose an efficient algorithm using inexact augmented Lagrangian multiplier (ALM) approach. Finally, we conduct extensive experiments on both BU-3DFE and Multi-PIE facial expression databases to evaluate the recognition performance of the proposed method. The experimental results confirm better recognition performance of the proposed method compared with the state of the art methods.