Face recognition system based on CNN and LBP features for classifier optimization and fusion

doi:10.19682/j.cnki.1005-8885.2018.0004

中国邮电高校学报(英文) ›› 2018, Vol. 25 ›› Issue (1): 37-47.doi: 10.19682/j.cnki.1005-8885.2018.0004

• Artificial Intelligence • 上一篇下一篇

Face recognition system based on CNN and LBP features for classifier optimization and fusion

吴雨林,江铭炎

山东大学

收稿日期:2017-08-23 修回日期:2018-01-16 出版日期:2018-02-28 发布日期:2018-02-28
通讯作者: 江铭炎 E-mail:jiangmingyan@sdu.edu.cn
基金资助:
山东省自然科学基金;中国自然科学基金

Face recognition system based on CNN and LBP features for classifier optimization and fusion

Received:2017-08-23 Revised:2018-01-16 Online:2018-02-28 Published:2018-02-28
Supported by:
;Natural Science Foundation of China

摘要/Abstract

摘要： Face recognition has been a hot-topic in the field of pattern recognition where feature extraction and classification play an important role. However, convolutional neural network (CNN) and local binary pattern (LBP) can only extract single features of facial images, and fail to select the optimal classifier. To deal with the problem of classifier parameter optimization, two structures based on the support vector machine (SVM) optimized by artificial bee colony (ABC) algorithm are proposed to classify CNN and LBP features separately. In order to solve the single feature problem, a fusion system based on CNN and LBP features is proposed. The facial features can be better represented by extracting and fusing the global and local information of face images. We achieve the goal by fusing the outputs of feature classifiers. Explicit experimental results on Olivetti Research Laboratory (ORL) and face recognition technology (FERET) databases show the superiority of proposed approaches.

关键词: CNN features, LBP features, classifier optimization, fusion system, face recognition

Abstract: Face recognition has been a hot-topic in the field of pattern recognition where feature extraction and classification play an important role. However, convolutional neural network (CNN) and local binary pattern (LBP) can only extract single features of facial images, and fail to select the optimal classifier. To deal with the problem of classifier parameter optimization, two structures based on the support vector machine (SVM) optimized by artificial bee colony (ABC) algorithm are proposed to classify CNN and LBP features separately. In order to solve the single feature problem, a fusion system based on CNN and LBP features is proposed. The facial features can be better represented by extracting and fusing the global and local information of face images. We achieve the goal by fusing the outputs of feature classifiers. Explicit experimental results on Olivetti Research Laboratory (ORL) and face recognition technology (FERET) databases show the superiority of proposed approaches.

Key words: CNN features, LBP features, classifier optimization, fusion system, face recognition

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Face recognition system based on CNN and LBP features for classifier optimization and fusion

Face recognition system based on CNN and LBP features for classifier optimization and fusion

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