基于选择性集成的表情识别方法  

Facial expression recognition method based on selective ensemble learning

作　　者：杨勇[1,2] 郭艳[1] 金裕成 黄勇[1] 

机构地区：[1]重庆邮电大学计算智能重庆市重点实验室,重庆400065 [2]韩国仁荷大学情报通信工学部

出　　处：《计算机工程与设计》2017年第3期773-778,共6页Computer Engineering and Design

基　　金：韩国科学与信息科技未来规划部2013年ICT研发基金项目(10039149);重庆市自然科学基金项目(CSTC;2007BB2445);2015年重庆市研究生科研创新基金项目(CYS15174)

摘　　要：为提高基于选择性集成的表情识别方法的泛化能力,降低其预测成本,提出一种基于带极值扰动的简化粒子群优化和选择性集成的表情识别方法。采用简化粒子群优化算法对基分类器进行选择集成,将基于选择性集成的表情识别问题转化为半定规划问题进行解决。对比实验结果表明,该方法比传统方法有更好的识别率和鲁棒性。To improve the generalization ability and reduce the prediction cost of the facial recognition method based on selective ensemble,an emotion recognition method based on extremum disturbed and simplified particle swarm optimization（tsPSO）algorithm and selective ensemble was proposed.Simplified particle swarm optimization algorithm was used to select the base classifiers and ensemble,the issue of facial expression recognition based on selective ensemble was transformed to a semi-definite programming problem.The comparative experimental results show that the proposed method is superior to the traditional methods and it is more robust.

关 键 词：集成学习 选择性集成 半定规划 简化粒子群优化 表情识别 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]