Face recognition based on Gabor transform and variable precision rough set

doi:10.3778/j.issn.1002-8331.2008.22.059

Computer Engineering and Applications ›› 2008, Vol. 44 ›› Issue (22): 197-200.DOI: 10.3778/j.issn.1002-8331.2008.22.059

• 图形、图像、模式识别 • Previous Articles Next Articles

Face recognition based on Gabor transform and variable precision rough set

LIU Hai-xia¹,ZHOU Jun¹,MEI Hong-yan¹,LIU Li-jun²

1.School of Computer Science and Engineering，Liaoning University of Technology，Jinzhou，Liaoning 121001，China
2.Jinzhou Branch，Liaoning Mobile Communication Ltd.，Jinzhou，Liaoning 121000，China

Received:2007-10-11 Revised:2008-01-21 Online:2008-07-11 Published:2008-07-11
Contact: LIU Hai-xia

基于Gabor变换和变精度粗糙集的人脸识别

刘海霞¹,周军¹,梅红岩¹,刘立军²

1.辽宁工业大学计算机科学与工程学院，辽宁锦州 121001
2.辽宁移动通信有限公司锦州分公司，辽宁锦州 121000

通讯作者: 刘海霞

Abstract

Abstract: In order to overcome the shortcoming of PCA，a method for extracting Gabor features of face images based on variable precision rough set is presented.First，Gabor features are extracted from face images.After reduced by 2DPCA algorithm，the features are reduced further by rough set.Then the nearest classifier is trained for classification.The experiments being performed on ORL human face image database show the method presented in this paper is superior to that of PCA algorithm and Gabor_PCA algorithm and so on.

Key words: Gabor transform, Principal Component Analysis（PCA）, variable precision rough set, face recognition

摘要： 针对主成分分析（PCA）方法在特征提取和降维方面的不足，提出一种基于变精度粗糙集的人脸Gabor特征筛选方法。首先提取人脸图像Gabor特征向量，经2DPCA方法处理后用粗糙集对其进行最佳特征选择。然后训练最近邻分类器进行分类识别。在ORL人脸库上验证了该方法的有效性。实验结果显示，该方法的分类能力优于PCA和Gabor_PCA等方法。

关键词: Gabor变换, 主成分分析, 变精度粗糙集, 人脸识别

LIU Hai-xia¹,ZHOU Jun¹,MEI Hong-yan¹,LIU Li-jun². Face recognition based on Gabor transform and variable precision rough set[J]. Computer Engineering and Applications, 2008, 44(22): 197-200.

刘海霞¹,周军¹,梅红岩¹,刘立军². 基于Gabor变换和变精度粗糙集的人脸识别[J]. 计算机工程与应用, 2008, 44(22): 197-200.

[1]	QIAO Hui, ZHOU Shuisheng. Nolinear Angle 2DPCA and Its Application on Face Recognition [J]. Computer Engineering and Applications, 2021, 57(8): 112-118.
[2]	HU Wentao, CHEN Xiuhong. Low-Rank Projection Learning Based on Neighbor Graph [J]. Computer Engineering and Applications, 2021, 57(7): 209-214.
[3]	WANG Peng, YE Xueyi, WANG Tao, QIAN Dingwei. Face Recognition Based on Double Variation and Double Space Local Directional Pattern [J]. Computer Engineering and Applications, 2021, 57(4): 91-99.
[4]	YU Duo, HUANG Yongdong. Hyperspectral Image Classification Based on SPCA and Domain Transform Recursive Filtering [J]. Computer Engineering and Applications, 2021, 57(4): 199-208.
[5]	SHI Zhengyu, CHEN Renwen, HUANG Bin. Low-Resolution Face Recognition Based on Self-Normalizing Neural Network [J]. Computer Engineering and Applications, 2021, 57(3): 137-143.
[6]	LI Junyao, LI Zhihui, XIE Lanchi, HOU Xinyu, YE Dong. Advances of Age Progression Based Cross-Age Face Recognition [J]. Computer Engineering and Applications, 2021, 57(24): 27-38.
[7]	LIU Jinjin, LI Qingbao, LI Xiaonan. Neural Network Compression Algorithm Based on Adversarial Learning and Knowledge Distillation [J]. Computer Engineering and Applications, 2021, 57(21): 180-187.
[8]	XU Xialing, LIU Tao, TIAN Guohui, YU Wenjuan, XIAO Dajun, LIANG Shanpeng. Review of Occlusion Face Recognition Methods [J]. Computer Engineering and Applications, 2021, 57(17): 46-60.
[9]	XU Ranran, WU Xiaojun, YIN Hefeng. Face Recognition via Discriminative Non-negative Representation Based Classification [J]. Computer Engineering and Applications, 2021, 57(13): 147-153.
[10]	WANG Yu, LIU Fan, WANG Fei. Autoencoder Based Sparse Representation for Single Sample Face Recognition [J]. Computer Engineering and Applications, 2021, 57(1): 168-172.
[11]	DONG Yanhua, ZHANG Shumei, ZHAO Junli. Review of Occlusion Face Recognition Method [J]. Computer Engineering and Applications, 2020, 56(9): 1-12.
[12]	LIN Kezheng, ZHANG Yuanming, LI Haotian. Research on HOG Feature Extraction Algorithm Weighted by Information Entropy [J]. Computer Engineering and Applications, 2020, 56(6): 147-152.
[13]	YUAN Xiaoping, CUI Qiwen, CHENG Gan, ZHANG Xia, ZHANG Yi, WANG Suyuan. Improved Gabor Self Quotient Image Algorithm and Its Application to Face Recognition [J]. Computer Engineering and Applications, 2020, 56(5): 206-213.
[14]	ZHANG Feixiang, YU Xueru, HE Weifeng, LI Chen. Face Recognition with Improved Loss Function and Multiple-Norm [J]. Computer Engineering and Applications, 2020, 56(24): 144-150.
[15]	HUANG Guangjun, DENG Yuanlong. Polarizer Visual Defect Detection and Classification Based on Improved LBP and SVM Algorithm [J]. Computer Engineering and Applications, 2020, 56(22): 251-255.

Face recognition based on Gabor transform and variable precision rough set

基于Gabor变换和变精度粗糙集的人脸识别

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics