基于监督超向量编码和自适应GMM的人脸表情识别方法

A Face Expression Recognition Method Based on Fusion of Supervised Super-vector Encoding and Adaptive GMM Model

针对不同状态和光照条件下的人脸表情识别问题,提出一种基于自适应高斯混合模型（GMM）融合监督式超级向量编码算法。首先,提取重叠图像块;然后,通过自适应GMM提取每个图像块的局部描述子,将图像低维特征映射到高维空间;最后,利用有监督的超级向量编码完成人脸表情识别。在Multi-PIE和BU3D-FE多视点人脸表情数据库上的实验结果显示,本算法在Multi-PIE和BU3D-FE人脸库上的识别率可分别高达91.8%、95.6%,识别一个样本所耗时间仅为0.142 s。相比其他几种较新的算法,本算法取得了更高的识别率,且大大降低了识别所耗时间。

Facial expression recognition under different lighting conditions and states is a challenging research. A fusion algorithm based on adaptive Gaussian Mixture Model （GMM） and supervised super-vector encoding is proposed. Firstly, the overlapping image blocks are extracted. Then, local descriptor from each block is extracted by the adaptive GMM so as to map images in lowdimensional space to high-dimensional space. Finally, supervised super-vector encoding is used to do classification training. Experimental results on the Multi-PIE and BU3D-FE multi-view facial expression databases show that the recognition accuracy of proposed algorithm can achieve 91.8% and 95.6% respectively on Multi-PIE and BU3D-FE. It takes only 0. 142 seconds in identifying a sample on BU3D-FE. Proposed algorithm has higher recognition accuracy and less recognition time-consuming than several other excellent algorithms.