计算机应用与软件2018，Vol.35Issue(5)：218-223,6.DOI:10.3969/j.issn.1000-386x.2018.05.039
融合特征基于深度多核学习的动态表情识别
DYNAMIC EXPRESSION RECOGNITION BASED ON DEEP MULTIPLE KERNEL LEARNING AND FEATURE FUSION
摘要
Abstract
In the process of dealing with multi-frame expression images in traditional dynamic expression recognition methods,we usually face some problems which lead to high feature dimension, simple feature category and difficult classifier selection in heterogeneous feature data.In order to improve the recognition rate, we firstly apply the slow feature analysis to detect the peak frame automatically among the expression sequences.Then we extract the geometric features and Gabor features based on the peak frame, and the feature dimension is reduced after Gabor features are extracted.Finally,the Deep Multiple Kernel Learning method is used to learn and classify the heterogeneous features which are fused by geometric features and Gabor features.Experiments show that the recognition rate has reached 94.4%by using the Extended Cohn-Kanade Dataset(CK +).关键词
慢特征分析/峰值帧/特征融合/深度多核学习Key words
Slow feature analysis/Peak frame/Feature fusion/Deep multiple kernel learning分类
信息技术与安全科学引用本文复制引用
何秀玲,蒋朗,吴珂,高倩..融合特征基于深度多核学习的动态表情识别[J].计算机应用与软件,2018,35(5):218-223,6.基金项目
教育部人文社会科学研究规划基金项目(17YJA880030) （17YJA880030）
华中师范大学中央高校基本科研业务费项目(CCNU15A05012). （CCNU15A05012）
