计算机工程与应用2017，Vol.53Issue(1)：158-162,5.DOI:10.3778/j.issn.1002-8331.1504-0117
自适应加权LGCP与快速稀疏表示的面部表情识别
Facial expression recognition with adaptive weighted LGCP and fast sparse repre-sentation
摘要
Abstract
The traditional LBP feature extraction of the image is sensitive to the change of the non-monotonic light. The global feature can’t be sparsely expressed by the LBP. An adaptive weighted Local Gray Code Patterns(LGCP) and fast sparse representation of feature extraction methods is proposed. The edge detection operator is used to maximize the edge values of the original image to overcome the influence of feature description from the light changes. Eight bit gray code is got by using LGCP and is converted into decimal. The optimal representation of local features will be got by the weighted cascade block. Distribution characteristics descriptor of the cascade histogram is as the atoms to form the dictionary. The global feature of the image would have better sparse representation. Finally, a fast sparse representation is selected as a classifier for classification. Several experiments on the extended Cohn-Kanade(CK+) expression data set show that the method has a rapid recognition, and the recognition rate is up to 94%.关键词
表情识别/格雷码模式/稀疏表示Key words
expression recognition/Gray Code Pattern(GCP)/sparse representation分类
信息技术与安全科学引用本文复制引用
吉训生,王荣飞..自适应加权LGCP与快速稀疏表示的面部表情识别[J].计算机工程与应用,2017,53(1):158-162,5.基金项目
国家自然科学基金（No.61170120）。 （）
