基于VGGNet和多谱带循环网络的高光谱人脸识别系统

doi:10.11772/j.issn.1001-9081.2018081788

计算机应用 ›› 2019, Vol. 39 ›› Issue (2): 388-391.DOI: 10.11772/j.issn.1001-9081.2018081788

基于VGGNet和多谱带循环网络的高光谱人脸识别系统

谢志华, 江鹏, 余新河, 张帅

江西省光电子与通信重点实验室(江西科技师范大学), 南昌 330031

收稿日期:2018-08-28 修回日期:2018-10-26 出版日期:2019-02-10 发布日期:2019-02-15
通讯作者: 谢志华
作者简介:谢志华(1977-),男,江西吉安人,副教授,博士,CCF会员,主要研究方向:生物识别、机器学习;江鹏(1990-),男,河南信阳人,硕士研究生,主要研究方向:图像处理、机器学习;余新河(1994-),男,湖北黄冈人,硕士研究生,主要研究方向:人脸识别、机器学习;张帅(1993-),男,山西长治人,硕士研究生,主要研究方向:图像处理、模式识别。
基金资助:
国家自然科学基金资助项目（61861020，61562063）；江西省自然科学基金资助项目（20171BAB202006）；江西省教育厅科技项目（GJJ160767）；江西科技师范大学青年拔尖人才项目（2013QNBJRC005）。

Hyperspectral face recognition system based on VGGNet and multi-band recurrent network

XIE Zhihua, JIANG Peng, YU Xinhe, ZHANG Shuai

Key Lab of Photoelectronics and Communication of Jiangxi Province(Jiangxi Science and Technology Normal University), Nanchang Jiangxi 330031, China

Received:2018-08-28 Revised:2018-10-26 Online:2019-02-10 Published:2019-02-15
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61861020, 61562063), the Natural Science Foundation of Jiangxi Province (20171BAB202006), the Science & Technology Project of Education Bureau of Jiangxi Province (GJJ160767), the Young Talent Project of Jiangxi Science and Technology Normal University (2013QNBJRC005).

摘要/Abstract

摘要： 为了提高光谱人脸数据表征人脸特征的有效性，提出一种基于VGGNet和多谱带循环训练的高光谱人脸识别方法。首先，在光谱人脸图像的预处理阶段，采用多任务卷积神经网络（MTCNN）进行高光谱人脸图像的精确定位，并利用混合通道的方式对高光谱人脸数据进行增强；然后，基于卷积神经网络（CNN）结构建立一个面向高光谱人脸识别的VGG12深度网络；最后，基于高光谱人脸数据的特点，引入多谱带循环训练方法训练建立的VGG12网络，完成最后的训练和识别。在公开的UWA-HSFD和PolyU-HSFD高光谱人脸数据集的实验结果表明，所提方法取得了比其他深度网络（如DeepID、DeepFace、VGGNet）更好的识别性能。

关键词: 高光谱人脸识别, 卷积神经网络, VGGNet, 多谱带循环训练, 深度神经网络

Abstract: To improve the effectiveness of facial feature represented by hyperspectral face data, a VGGNet and multi-band recurrent training based method for hyperspectral face recognition was proposed. Firstly, a Multi-Task Convolutional Neural Network (MTCNN) was used to locate the hyperspectral face image accurately in preprocessing phase, and the hyperspectral face data was enhanced by mixed channel. Then, a Convolutional Neural Network (CNN) structure based VGG12 deep network was built for hyperspectral face recognition. Finally, multi-band recurrent training was introduced to train the VGG12 network and realize the recognition based on the characteristics of hyperspectral face data. The experimental results of UWA-HSFD and PolyU-HSFD databases reveal that the proposed method is superior to other deep networks such as DeepID, DeepFace and VGGNet.

Key words: hyperspectral face recognition, Convolutional Neural Network (CNN), VGGNet, multi-band recurrent training, Deep Neural Network (DNN)

中图分类号:

谢志华, 江鹏, 余新河, 张帅. 基于VGGNet和多谱带循环网络的高光谱人脸识别系统[J]. 计算机应用, 2019, 39(2): 388-391.

XIE Zhihua, JIANG Peng, YU Xinhe, ZHANG Shuai. Hyperspectral face recognition system based on VGGNet and multi-band recurrent network[J]. Journal of Computer Applications, 2019, 39(2): 388-391.

参考文献

[1] OSIA N, BOURLAI T. A spectral independent approach for physiological and geometric based face recognition in the visible, middle-wave and long-wave infrared bands[J]. Image and Vision Computing, 2014, 32(11):847-859.
[2] PAN Z, HEALEY G E, PRASCAD M, et al. Face recognition in hyperspectral images[C]//Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2003:334-339.
[3] CHANG H, KOSCHAN A, ABIDI B, et al. Fusing continuous spectral images for face recognition under indoor and outdoor illuminants[J]. Machine Vision and Applications, 2010, 21(2):201-215.
[4] DI W, ZHANG L, ZHANG D. Studies on hyperspectral face recognition in visible spectrum with feature band selection[J]. IEEE Transactions on Systems, Man and Cybernetics-Part A:Systems and Humans, 2010, 40(6):1354-1361.
[5] UZAIR M, MAHMOOD A, MIAN A. Hyperspectral face recognition with spatiospectral information fusion and PLS regression[J]. IEEE Transactions on Image Processing, 2015, 24(3):1127-1137.
[6] 魏冬梅, 张立人, 胡楠楠,等. 联合空谱信息和Gabor特征的高光谱人脸识别算法[J]. 北京理工大学学报,2017,37(10):1077-1083. (WEI D M, ZHANG L R, HU N N, et al. Hyperspectral face recognition with spatial-spectral fusion information and Gabor feature[J]. Transactions of Beijing Institute of Technology, 2017, 37(10):1077-1083.)
[7] GHASEMZADEH A, DEMIREL H. 3D discrete wavelet transform-based feature extraction for hyperspectral face recognition[J]. IET Biometrics, 2018, 7(1):49-55
[8] ZHANG K, ZHANG Z, LI Z, et al. Joint face detection and alignment using multi-task cascaded convolution networks[J]. IEEE Signal Processing Letters, 2016, 23(10):1499-1503.
[9] 杨楠, 南琳, 张丁一, 等. 基于深度学习的图像描述研究[J]. 红外与激光工程,2018,47(2):203002. (YANG N, NAN L, ZHANG D Y, et al. Research on image interpretation based on deep learning[J]. Infrared and Laser Engineering, 2018, 47(2):203002.)
[10] SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]//Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Piscataway, NJ:IEEE, 2015:1-9.
[11] SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL].[2018-03-22]. https://arxiv.org/pdf/1409.1556.pdf.
[12] 张国山, 张培崇, 王欣博. 基于多层次特征差异图的视觉场景识[J]. 红外与激光工程, 2018, 47(2):203004. (ZHANG G S, ZHANG P C, WANG X B, Visual place recognition based on multi-level feature difference map[J]. Infrared and Laser Engineering, 2018, 47(2):203004.)
[13] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[C]//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada:Curran Associates Inc., 2012, 1:1097-1105.
[14] MOLLAHOSSEINI A, CHAN D, MAHOOR M H. Going deeper in facial expression recognition using deep neural networks[C]//Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision. Piscataway, NJ:IEEE, 2016:1-10.
[15] SUN Y, WANG X, TANG X O. Deep learning face representation from predicting 10000 classes[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2014:1891-1898.
[16] TAIGMAN Y, YANG M, RANZATO M, et al. DeepFace:closing the gap to human-level performance in face verification[C]//Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Washington, DC:IEEE Computer Society, 2014:1701-1708.
[17] IOFFE S, SZEGEDY C. Batch normalization:accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on Machine Learning.[S.l.]:ICML, 2015:448-456.

基于VGGNet和多谱带循环网络的高光谱人脸识别系统

Hyperspectral face recognition system based on VGGNet and multi-band recurrent network

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