摘要
为克服人脸表情图像识别过程中光照、遮挡等带来的影响,减少稀疏表示分类的时间,提出一种融合HOG特征和改进KC-FDDL(K-means Cluster and Fisher Discrimination Dictionary Learning)字典学习稀疏表示的人脸表情识别算法。对归一化后的表情图像提取HOG特征构成训练集,对训练集进行改进的K-均值聚类的Fisher判别字典学习,利用残差加权的稀疏表示进行表情分类。Cohn-Kanade数据库上的实验结果表明,该算法相比其他的人脸表情分类方法具有耗时低、相似表情分类更准确的优势。
In order to overcome the problems induced by illumination andocclude in facial expression recognition and reduce the time required by sparse representation classification, the facial expression recognition algorithm withfusion of HOG feature and improved KC-FDDL dictionary learning sparserepresentation is put forward. Improved K-means cluster and Fisher discriminationdictionary learningis implemented on thetraining setgenerated by extracting HOG features of normalized expression image.Facial expression classification is conducted by thesparse representation with weighted residuals. Experimental results on the Cohn-Kanade databaseshow that this method is lower time-consumingand more accurate for similar facial expression classification than other facial expression classification methods.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2018年第1期28-35,44,共9页
Journal of System Simulation
基金
国家自然科学基金(61262019)
