基于概率协作表示的多表情序列融合识别  

Multi-Expression Sequence Fusion Recognition Based on Probabilistic Cooperative Representation

作　　者：王秀友[1,2] 范建中[1] 刘华明[1] 徐冬青[1] 刘争艳[1] Wang Xiuyou;Fan Jianzhong;Liu Huaming;Xu Dongqing;Liu Zhengyan(School of Computer and Information Engineering, Fuyang Normal University, Fuyayig,Anhui 236037,China;School of Computer Science and Technology, Anhui University, Hefei,Anhui 230601,China)

机构地区：[1]阜阳师范学院计算机与信息工程学院,安徽阜阳236037 [2]安徽大学计算机科学与技术学院,安徽合肥230601

出　　处：《激光与光电子学进展》2019年第13期79-85,共7页Laser & Optoelectronics Progress

基　　金：安徽省高校优秀青年骨干人才项目(gxfx2017072);安徽省自然科学基金(1708085MF155);阜阳市政府-阜阳师范学院横向合作科研项目(XDHX2016020,XDHX201710);安徽省教育厅自然科学研究重点项目(KJ2018A0345);阜阳师范学院青年人才基金重点项目(rcxm201706)

摘　　要：传统表情识别往往是基于单一图像进行特征提取、训练及识别,缺乏在动态时间上的细微表情变化提取。利用时间前后的人脸表情变化信息,提出了一种基于概率协作表示的多视频序列融合表情识别方法。先采用主动外观模型(AAM)定位出人脸表情的68个特征点,利用提出的融合策略将相邻3帧表情图像的AAM特征进行融合,最后利用概率协作表示的分类优势进行识别。实验结果表明,在CK+表情数据库上,所提出的方法能够把握表情的时间变化信息,相比于近几年的表情识别算法,在识别率上取得了较好的效果。Traditional facial expression recognition often uses a single image to extract features, train, and recognize;however, subtle changes in dynamic facial expressions are not recognized. This study proposes a multi- expression sequence fusion recognition method based on probabilistic cooperative representation using the changes in facial expression before and after time. First, 68 feature points of facial expression are located using an active appearance model (AAM). Then, the AAM features of three adjacent facial expressions are combined using the the proposed method. Finally, the classification advantages of probabilistic cooperative representation are used for recognition. Experimental results indicate that the proposed method can grasp the temporal change information of expression on the CK + expression database. Moreover, this method can achieve higher recognition rates compared with traditional expression recognition algorithms.

关 键 词：机器视觉 人脸表情识别 主动外观模型 多序列融合 概率协作表示 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]