Non-negative matrix factorization and its applications in facial expression recognition

doi:10.3778/j.issn.1002-8331.2010.26.056

Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (26): 182-183.DOI: 10.3778/j.issn.1002-8331.2010.26.056

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

Non-negative matrix factorization and its applications in facial expression recognition

HUANG Yong

Department of Electronic Engineering，Liuzhou Railway Vocational Technical College，Liuzhou，Guangxi 545007，China

Received:2009-02-24 Revised:2009-04-21 Online:2010-09-11 Published:2010-09-11
Contact: HUANG Yong

非负矩阵因子及在人脸表情识别中的应用

黄勇

柳州铁道职业技术学院电子工程系，广西柳州 545007

通讯作者: 黄勇

Abstract

Abstract: A facial expression recognition method based on 2-Dimensional Non-negative Matrix Factorization（2DNMF） is proposed in this paper.2DNMF，which regards the 2D original expression images as 2D matrices and represents them with a set of 2D bases via appling NMF.Unlike NMF，2DNMF takes full advantage of the information between rows and columns of the image，thus remains the original expression information as much as possible.Experimental result on CED-WYU（1.0）and JAFFE shows that it is an effective method for improving the recognition accuracy and effectiveness.

Key words: Non-negative Matrix Factorization（NMF）, 2-Dimensional Non-negative Matrix Factorization（2DNMF）, expression recognition

摘要： 提出了一种基于二维非负矩阵因子的人脸表情识别方法。该算法直接将2维人脸表情图像矩阵作为2维矩阵并结合NMF进行表情特征提取，称之为2DNMF。与NMF等不同，2DNMF充分利用表情图像矩阵中的行向量间的信息和列向量间的信息，尽可能地保留了原始的表情信息。基于CED-WYU（1.0）和JAFFE两个表情数据库的识别结果表明，基于2维非负矩阵因子的特征提取方法能有效地提高识别率及效率。

关键词: 非负矩阵因子, 2维非负矩阵因子, 表情识别

CLC Number:

TP391.41

HUANG Yong . Non-negative matrix factorization and its applications in facial expression recognition[J]. Computer Engineering and Applications, 2010, 46(26): 182-183.

黄勇. 非负矩阵因子及在人脸表情识别中的应用[J]. 计算机工程与应用, 2010, 46(26): 182-183.

[1]	JIANG Bin, ZHONG Rui, ZHANG Qiuwen, ZHANG Huanlong. Survey of Non-frontal Facial Expression Recognition by Using Deep Learning Methods [J]. Computer Engineering and Applications, 2021, 57(8): 48-61.
[2]	WEN Jiebin, YANG Wenzhong, MA Guoxiang, ZHANG Zhihao, LI Hailei. Micro-expression Recognition Based on Apex Frame Optical Flow and Convolutional Autoencoder [J]. Computer Engineering and Applications, 2021, 57(4): 127-133.
[3]	YANG Luyue, ZHANG Shumei, ZHAO Junli. Dynamic Expression Recognition with Partial Occlusion Based on Parallel Gan [J]. Computer Engineering and Applications, 2021, 57(24): 168-178.
[4]	CUI Ziyue, PI Jiatian, CHEN Yong, YANG Jiezhi, XIAN Yan, WU Zhiyou, ZHAO Lijun, ZENG Shaohua, LYU Jia. Facial Expression Recognition Combined with Improved VGGNet and Focal Loss [J]. Computer Engineering and Applications, 2021, 57(19): 171-178.
[5]	ZHANG Hongli, BAI Xiangyu. Facial Expression Recognition Method Using Optimized Pruning GoogLeNet [J]. Computer Engineering and Applications, 2021, 57(19): 179-188.
[6]	ZHANG Liwei, WANG Sujing, DUAN Xianhua. Fine-Grained Hierarchical Spatiotemporal Descriptors for Micro-Expression Recognition [J]. Computer Engineering and Applications, 2021, 57(13): 154-160.
[7]	ZHANG Ren, HE Ning. A Survey of Micro-Expression Recognition Methods [J]. Computer Engineering and Applications, 2021, 57(1): 38-47.
[8]	SUN Xiao, DING Xiaolong. Data Augmentation Method Based on Generative Adversarial Networks for Facial Expression Recognition Sets [J]. Computer Engineering and Applications, 2020, 56(4): 115-121.
[9]	ZHANG Aimei, XU Yang. Attention Hierarchical Bilinear Pooling Residual Network for Expression Recognition [J]. Computer Engineering and Applications, 2020, 56(23): 161-166.
[10]	ZHANG Yanliang, LU Bing, JIANG Hanxiao, HONG Xiaopeng, ZHAO Guoying, ZHANG Weitao. Study on Investigating Correlation Between Categories of Micro-expression and Corresponded Regions [J]. Computer Engineering and Applications, 2020, 56(19): 146-151.
[11]	SUI Xiuwu, NIU Jiabao, LI Haotian, QIAO Mingmin. Upper Limb sEMG Gesture Recognition Method Based on NMF-SVM Model [J]. Computer Engineering and Applications, 2020, 56(17): 161-166.
[12]	LIANG Huagang, WANG Yaru, ZHANG Zhiwei. Facial Expression Recognition Based on Res-Bi-LSTM [J]. Computer Engineering and Applications, 2020, 56(13): 204-209.
[13]	LV Hui, TONG Qianqian, YUAN Zhiyong. Realtime Architecture for Facial Expression Recognitionin Complex Scenes Based on Face Region Segmentation [J]. Computer Engineering and Applications, 2020, 56(12): 134-140.
[14]	LIANG Huagang, ZHANG Zhiwei, WANG Yaru. Expression Recognition Method for Adaptive Gabor Convolution Kernel Coding Network [J]. Computer Engineering and Applications, 2020, 56(10): 149-156.
[15]	RUAN Kai, QIU Weigen. Study on Facial Expression Recognition Algorithm of Multi-Information Fusion Based on Deep Learning [J]. Computer Engineering and Applications, 2019, 55(5): 192-196.

Non-negative matrix factorization and its applications in facial expression recognition

非负矩阵因子及在人脸表情识别中的应用

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics