基于词包模型和SURF局部特征的人脸识别

doi:10.3969/j.issn.1000-1565.2017.04.013基于词包模型和SURF局部特征的人脸识别

河北大学学报(自然科学版) ›› 2017, Vol. 37 ›› Issue (4): 411-418.DOI: 10.3969/j.issn.1000-1565.2017.04.013基于词包模型和SURF局部特征的人脸识别

基于词包模型和SURF局部特征的人脸识别

刘翠响,李敏,张凤林

收稿日期:2016-09-29 出版日期:2017-07-25 发布日期:2017-07-25
通讯作者: 李敏(1990—),女,山东德州人,河北工业大学硕士研究生.主要从事图像处理与模式识别研究.E-mail:1607708753@qq.com
作者简介:刘翠响(1973—),女,河北辛集人,河北工业大学副教授,博士,主要从事信号处理与模式识别研究. E-mail:liucuix@126.com
基金资助:
国家自然科学基金资助项目(61203245)

Face recognition based on BOW and SURF local features

LIU Cuixiang, LI Min, ZHANG Fenglin

School of Electronics Information Engineering, Hebei University of Technology, Tianjin 300401, China

Received:2016-09-29 Online:2017-07-25 Published:2017-07-25

摘要/Abstract

摘要： 针对传统人脸识别方法实时性差的缺点,提出了一种加速鲁棒性特征(SURF,speed up robust features)和词包模型(BOW,bag-of-word)相结合的人脸识别方法.图像经过预处理后,使用SURF算法自动提取图像的关键点和相应的特征描述符,再进一步用BOW方法将其编成视觉单词作为人脸的局部特征.最后,采用K最邻近结点算法进行分类识别.使用了2个数据集验证了提出的方法——标准CMU-PIE(卡内基梅隆大学——姿势、光照、表情人脸数据库)人脸库和采集的数据库,分别达到了97.5%和99.3%的识别率,而且特征提取的时间少于0.108 s,识别的时间少于0.017 s.结果表明,本文提出的算法不仅精确而且快速,具有更好的稳定性和有效性.

关键词: 人脸识别, 词包模型, SURF, 局部特征, K-NN

Abstract: To overcome the limitations of traditional face recognition methods for real-time, a face recognition method which based on speed up robust features and bag-of-word model was proposed.Image after preprocessing, we used SURF to extract key points of images and corresponding feature descriptors automatically.Further, bag-of word model was used to code the descriptors into visual words as local features of the face.Finally, K-Nearest Neighbor algorithm was adopted to recognize the human faces.The proposed method is validated with both CMU-PIE dataset and dataset collected in the laboratory.It can achieve 97.5% and 99.3% recognition rates on these two datasets, respectively.In average, it took less than 0.108 s for feature extraction and less than 0.017 s for matching.The results indicate that the proposed method not only precise moreover fast, and had better stability and effectiveness.

Key words: face recognition, bag-of-word model, SURF, local features, K-NN

中图分类号:

TP391

刘翠响,李敏,张凤林. 基于词包模型和SURF局部特征的人脸识别[J]. 河北大学学报(自然科学版), 2017, 37(4): 411-418.

LIU Cuixiang, LI Min, ZHANG Fenglin. Face recognition based on BOW and SURF local features[J]. Journal of Hebei University (Natural Science Edition), 2017, 37(4): 411-418.

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基于词包模型和SURF局部特征的人脸识别

Face recognition based on BOW and SURF local features

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