[1]潘峥嵘,贺秀伟.人脸表情识别在智能机器人中的应用研究[J].计算机技术与发展,2018,28(02):173-177.[doi:10．3969/j．issn．1673－629X．2018．02．037]
　PAN Zheng-rong,HE Xiu-wei.Research on Application of Facial Expression Ｒecognition in Intelligent Ｒobot[J].,2018,28(02):173-177.[doi:10．3969/j．issn．1673－629X．2018．02．037]
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人脸表情识别在智能机器人中的应用研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年02期
页码:
173-177
栏目:
应用开发研究
出版日期:
2018-02-10

文章信息/Info

Title:
Research on Application of Facial Expression Ｒecognition in Intelligent Ｒobot
文章编号:
1673－629X(2018)02－0173－05
作者:
潘峥嵘; 贺秀伟
兰州理工大学 电气与信息工程学院，甘肃 兰州 730050
Author(s):
PAN Zheng-rong; HE Xiu-wei
School of Electrical and Information Engineering，Lanzhou University of Technology，Lanzhou 730050，China
关键词:
人脸表情识别; BＲISK; 主动表观模型; 局部 Gabor 二进制模式; 机器人
Keywords:
facial expression recognition; BＲISK; active appearance models (AAM); local Gabor binary pattern (LGBP); robot
分类号:
TP391.4
DOI:
10．3969/j．issn．1673－629X．2018．02．037
文献标志码:
A
摘要:
随着智能机器人的快速发展，如何赋予机器人和谐的人机交互能力使其能够感知人类的情感成为当前人机交互研究的热点。针对 AAM 提取人脸表情特征时表征能力不足和实时性差的问题，提出一种基于 BＲISK 和 AAM 组合方式提取表情的形状和纹理特征的方法。首先对初始的人脸图像采用 Fast－SIC 算法拟合出人脸的 AAM 模型，在获得人脸关键特征点之后用 BＲISK 匹配特征点以增强匹配效率;其次用 LGBP 对人脸 AAM 模型的纹理特征进行提取以增强表情特征的表征能力;最后用 SVM 分类器对提取的表情特征进行分类。实验结果表明，BＲISK 与 AAM 组合的特征提取方法可以提高 AAM 模型的拟合速率，用 LGBP 提取的纹理特征更具有可分性。在 CK+ 和 LFPW 人脸库上验证了该算法对面部关键特征点的检测精度和效率，而且与其他算法相比取得了较高的表情识别率，最后在 NAO 机器人平台上验证了算法的实用性。
Abstract:
With the rapid development of intelligent robots，how to give robots harmonious human－computer interaction makes it able to perceive human emotion has been becoming the current hot topics in the study of human－computer interaction．According to the problem of insufficient representation of facial expression features and the poor performance of real－time feature extraction using traditional AAM model，we propose a novel method of extracting shape and texture features of facial expression based on the combination of BＲISK and AAM．Firstly，the Fast－SIC algorithm is used to fit out the AAM of face for the original face image．In order to enhance the efficiency of feature matching，BＲISK algorithm is used to match the acquired key facial feature points，and then LGBP is utilized to extract texture features of AAM in order to strengthen the representation of facial expression features．Finally，to classify the classes of facial expression features using the SVM classifier．The experiments show that the proposed method has improved the efficiency of fitting AAM，and texture features extracted by LGBP have more separability．The detection accuracy and efficiency of key facial feature points are validated respectively on the datasets of CK+ and LFPW，and this method has achieved high facial expression recognition rate compared with other algorithms．Finally，its practicability is verified on the platform of NAO robot．

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更新日期/Last Update: 2018-03-29