一种基于Curv-SAE特征融合的人脸降维和识别方法

doi:10.11896／j.issn.1002-137X.2018.10.049

Abstract

Abstract: Compared with the traditional dimension reduction algorithm,stacked autoencoders (SAE)in deep learning can effectively learn the features and achieve efficient dimension reduction,but its performance depends on the input characteristics.The second generation discrete curvelet transform can extract the information of human faces,including edge and overview features,and ensure that the input features of SAE are sufficient,thus making up for the shortages of SAE.Therefore,a new recognition and dimension reduction algorithm based on Curv-SAE feature fusion was proposed.Firstly,the face images are processed by DCT to generate the Curv-faces,which are trained as input characteristics of SAE.And then different layers of features are used for the final classification of identification.Experimental results on ORL and FERET face databases show that the feature information of curvelet transform is more abundant than the wavelet transform.Compared with the traditional dimension reduction algorithms,the feature expression of SAE is more complete and the recognition accuracy is higher.

Key words: Deep learning, Face recognition, The Second generation discrete curvelet transform, Stacked autoencoders, Dimension reduction

CLC Number:

TP391.41

ZHANG Zhi-yu, LIU Si-yuan. Method of Face Recognition and Dimension Reduction Based on Curv-SAE Feature Fusion[J].Computer Science, 2018, 45(10): 267-271.

0
/ / Recommend

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

URL: http://www.jsjkx.com/EN/10.11896／j.issn.1002-137X.2018.10.049

http://www.jsjkx.com/EN/Y2018/V45/I10/267

References

[1]CHENG J L,WECHSLER H.A Gabor Feature Classifier for Face Recognition[C]∥IEEE International Conference on Computer Vision.2001:270-275.
[2]CHIENJ T,WU C C.Discriminant Wavelet-faves and Nearest Feature classifiers for Face Recognition[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2002(24):1644-1649.
[3]HU Z H,SONG Y L.Image reduction and reconstruction based on autoencoder network [J].Journal of Electronic and Information Technology,2009,31(5):1189-1193.(in Chinese)
胡昭华,宋耀良.基于Autoencoder网络的数据降维和重构[J].电子与信息学报,2009,31(5):1189-1193.
[4]MEE D A,RAT A C.Enhanced Face Recognition Through Varia- tion of Principle Component Analysis(PCA)[C]∥International Conference on Industrial and Information.2007:347-351.
[5]BEL P N,HES J P,KRR D J,et al.Fisherfaces:Recognition Using Class Specific Linear Projection[J].IEEE Trans.on PAMI,1997,19(7):711-720.
[6]MART T,LEO M,D’ORAZIO.Facial Feature Estraction by Kernel Independent Component Analysis[C]∥IEEE Conference on Advanced Video and Signal Based Surveillance.2005:210-275.
[7]HU Z H,SONG Y L.Image reduction and reconstruction based on a continuous self-coding network[J].Data Collection and Processing,2010,25(3):318-324.(in Chinese)
胡昭华,宋耀良.基于一种连续自编码网络的图像降维和重构[J].数据采集与处理,2010,25(3):318-324.
[8]TEN J B,SILVA V D,LANGFO J C.A Global Geometric Framework for Nonlinear Dimensionality Reduction[J].Science,2000,290(5500):2319-2323.
[9]ROUEIS S T,SAUL L K.Nonlinear Dimensionality Reduction by Locally Linear Embedding[J].Science,2000,290(5500):2323-2326.
[10]LIANG W,DAVID S.Learning and Matching of Dynamic Shape Manifolds for Human Action Recognition[J].IEEE Transactions on Image Processing,2007,16(6):1646-1661.
[11]HINTON G E,OSIN S.A Fast Learning Algorithm for Deep Belief Nets[J].Neural Computation,2006,18(7):1527-1554.
[12]HINTON G E,SALAK R R.Reducing the Dimensionality of Data with Neural Networks[J].Science,2006,313(9):504-507.
[13]CANDES E J,DONOHO D L.Curvelet-A Surprisingly Effective Nonadaptive Representation for Objects with Edges[M]∥Curve and Suface Fitting:SaintMalo.TN:Vanderbilt Univ Press,1999.
[14]CANDES E J,DEMANET L,DONOHO D L.Fast Discrete Curvelet Transforms[R].Applied and Computational Mathematics.California:California Institute of Technology,2005.
[15]VINCENT P,LARO H,BENGIO Y,et al.Extracting and Composing Robust Features with Denoising Autoencoders[C]∥Proceedings of the 25^th International Conference on Mchine Lear-ning.2008:1096-1103.
[16]ZHU X T,ZHANG Y Z,WANG F D.Research on data dimension reduction method based on sparse self-coding network[J].Journal of Shenyang University of Technology,2016,35(5):39-44.(in Chinese)
朱啸天,张艳珠,王凡迪.一种基于稀疏自编码网络的数据降维方法研究[J].沈阳理工大学学报,2016,35(5):39-44.
[17]CHEN D Y.Research on dimension reduction algorithm of ma- nifold learning and its application in face recognition[D].Jiangsu:Jiangnan University,2014.(in Chinese)
陈达遥.流形学习降维算法研究及其在人脸识别中的应用[D].江苏:江南大学,2014.
[18]WANG H Y.Research on manifold learning feature extraction method and face recognition in subspace[D].Harbin:Harbin Polytechnic University,2017.(in Chinese)
王海燕.子空间的流形学习特征提取方法及人脸识别研究[D].哈尔滨:哈尔滨理工大学,2017.
[19]GUO J X,CHEN W.Feature recognition based on HOG multi-feature fusion and random forest[J].Computer Science,2013,40(10):279-284.(in Chinese)
郭金鑫,陈玮.基于HOG多特征融合与随机森林的人脸识别[J].计算机科学,2013,40(10):279-284.
[20]FADHLAN K Z,AMIR A S,YASIR M M.Robust Face Recognition Against Expressions and Partial Occlusions[J].International Journal of Automation and Computing,2016,13(4):319-337.

Related Articles 15

[1]	DONG Xiao-mei, WANG Rui, ZOU Xin-kai. Survey on Privacy Protection Solutions for Recommended Applications [J]. Computer Science, 2021, 48(9): 21-35.
[2]	ZHOU Xin-min, HU Yi-gui, LIU Wen-jie, SUN Rong-jun. Research on Urban Function Recognition Based on Multi-modal and Multi-level Data Fusion Method [J]. Computer Science, 2021, 48(9): 50-58.
[3]	QIAN Meng-wei , GUO Yi. Biased Deep Distance Factorization Algorithm for Top-N Recommendation [J]. Computer Science, 2021, 48(9): 103-109.
[4]	XU Tao, TIAN Chong-yang, LIU Cai-hua. Deep Learning for Abnormal Crowd Behavior Detection:A Review [J]. Computer Science, 2021, 48(9): 125-134.
[5]	ZHANG Xin-feng, SONG Bo. A Person Re-identification Method Based on Improved Triple Loss and Feature Fusion [J]. Computer Science, 2021, 48(9): 146-152.
[6]	LIN Zhen-xian, ZHANG Meng-kai, WU Cheng-mao, ZHENG Xing-ning. Face Image Inpainting with Generative Adversarial Network [J]. Computer Science, 2021, 48(9): 174-180.
[7]	HUANG Xiao-sheng, XU Jing. Multi-focus Image Fusion Method Based on PCANet in NSST Domain [J]. Computer Science, 2021, 48(9): 181-186.
[8]	CHEN Chang-wei, ZHOU Xiao-feng. Fast Local Collaborative Representation Based Classifier and Its Applications in Face Recognition [J]. Computer Science, 2021, 48(9): 208-215.
[9]	TIAN Ye, CHEN Hong-wei, WANG Fa-sheng, CHEN Xing-wen. Overview of SLAM Algorithms for Mobile Robots [J]. Computer Science, 2021, 48(9): 223-234.
[10]	XIE Liang-xu, LI Feng, XIE Jian-ping, XU Xiao-jun. Predicting Drug Molecular Properties Based on Ensembling Neural Networks Models [J]. Computer Science, 2021, 48(9): 251-256.
[11]	FENG Xia, HU Zhi-yi, LIU Cai-hua. Survey of Research Progress on Cross-modal Retrieval [J]. Computer Science, 2021, 48(8): 13-23.
[12]	WANG Li-mei, ZHU Xu-guang, WANG De-jia, ZHANG Yong, XING Chun-xiao. Study on Judicial Data Classification Method Based on Natural Language Processing Technologies [J]. Computer Science, 2021, 48(8): 80-85.
[13]	GUO Lin, LI Chen, CHEN Chen, ZHAO Rui, FAN Shi-lin, XU Xing-yu. Image Super-resolution Reconstruction Using Recursive ResidualNetwork Based on ChannelAttention [J]. Computer Science, 2021, 48(8): 139-144.
[14]	LIU Shuai, RUI Ting, HU Yu-cheng, YANG Cheng-song, WANG Dong. Monocular Visual Odometer Based on Deep Learning SuperGlue Algorithm [J]. Computer Science, 2021, 48(8): 157-161.
[15]	WANG Shi-yun, YANG Fan. Remote Sensing Image Semantic Segmentation Method Based on U-Net Feature Fusion Optimization Strategy [J]. Computer Science, 2021, 48(8): 162-168.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 10

[1]	LEI Li-hui and WANG Jing. Parallelization of LTL Model Checking Based on Possibility Measure[J]. Computer Science, 2018, 45(4): 71 -75 .
[2]	SUN Qi, JIN Yan, HE Kun and XU Ling-xuan. Hybrid Evolutionary Algorithm for Solving Mixed Capacitated General Routing Problem[J]. Computer Science, 2018, 45(4): 76 -82 .
[3]	ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[4]	WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[5]	SHI Wen-jun, WU Ji-gang and LUO Yu-chun. Fast and Efficient Scheduling Algorithms for Mobile Cloud Offloading[J]. Computer Science, 2018, 45(4): 94 -99 .
[6]	ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[7]	LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[8]	GENG Hai-jun, SHI Xin-gang, WANG Zhi-liang, YIN Xia and YIN Shao-ping. Energy-efficient Intra-domain Routing Algorithm Based on Directed Acyclic Graph[J]. Computer Science, 2018, 45(4): 112 -116 .
[9]	CUI Qiong, LI Jian-hua, WANG Hong and NAN Ming-li. Resilience Analysis Model of Networked Command Information System Based on Node Repairability[J]. Computer Science, 2018, 45(4): 117 -121 .
[10]	WANG Zhen-chao, HOU Huan-huan and LIAN Rui. Path Optimization Scheme for Restraining Degree of Disorder in CMT[J]. Computer Science, 2018, 45(4): 122 -125 .

Method of Face Recognition and Dimension Reduction Based on Curv-SAE Feature Fusion

PDF (PC)

Abstract

Cite this article

share this article

References

Related Articles 15

Metrics

Comments

Recommended 10