一种用于表情识别的局部判别简

 在判别分量分析算法的基础上，提出了一种针对人脸表情识别任务的局部判别分量分析算法.首先该算法为每个测试样本选取了一组近邻训练样本，获取了训练集的局部样本结构.然后在最大化判别样本子集协方差的同时，最小化样本子集内所有数据的协方差，从而有效地提取了测试样本的表情特征.在多个人脸表情数据库上的实验结果表明，该算法不但提高了判别分量分析算法的表情识别率，而且具有较强的鲁棒性.

A Local Discriminative Component Analysis Algorithm for Facial Expression Recognition

Based on discriminative component analysis(DCA)algorithm,a local discriminative component analysis(LDCA)algorithm for facial expression recognition is proposed.First,LDCA algorithm chooses a number of nearest neighbors of a test sample from a training set to capture the local data structure.Then,the facial expression features of each testing sample are extracted by maximizing the total variance between the discriminative data chunklets and minimizing the total variance of data instances in the same chunklets.The experimental results on several representative facial expression datasets show that proposed method not only improves the recognition rate of DCA algorithm,but also exhibits strong robustness.