基于生成式对抗网络的鲁棒人脸表情识别

姚乃明; 郭清沛; 乔逢春; 陈辉; 王宏安

doi:10.16383/j.aas.2018.c170477

基于生成式对抗网络的鲁棒人脸表情识别

doi: 10.16383/j.aas.2018.c170477

姚乃明^1,2,,
郭清沛^1,2,,
乔逢春^1,2,,
陈辉^1,2, ,,
王宏安^1,2,3,

1.
中国科学院软件研究所人机交互北京市重点实验室北京 100190
2.
中国科学院大学北京 100049
3.
中国科学院软件研究所计算机科学国家重点实验室北京 100190

基金项目:

国家自然科学基金 61572479

国家重点研发计划 2017YFB1002805

中国科学院前沿科学重点研究计划 QYZ DY-SSW-JSC041

国家自然科学基金 61661146002

详细信息

作者简介:
姚乃明  中国科学院软件研究所博士研究生.2011年获得首都师范大学硕士学位.主要研究方向为情感交互, 机器学习和计算机视觉.E-mail:naiming2014@iscas.ac.cn

郭清沛  中国科学院软件研究所硕士研究生.2014年获得华中科技大学学士学位.主要研究方向为机器学习, 计算机视觉和情感计算.E-mail:qingpei2014@iscas.ac.cn

乔逢春  中国科学院软件研究所硕士研究生.2016年获得北京林业大学学士学位.主要研究方向为深度学习和图像生成.E-mail:qiaofengchun16@mails.ucas.ac.cn

王宏安   中国科学院软件研究所研究员.1998年获得中国科学院软件研究所博士学位.主要研究方向为人机交互和实时智能.E-mail:hongan@iscas.ac.cn

通讯作者:
陈辉中国科学院软件研究所研究员.2006年获得香港中文大学计算机学院博士学位.主要研究方向为人机交互, 情感交互, 力触觉交互和虚拟现实.本文通信作者.E-mail:chenhui@iscas.ac.cn

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出版历程
- 收稿日期: 2017-08-30
- 录用日期: 2018-02-07
- 刊出日期: 2018-05-20

Robust Facial Expression Recognition With Generative Adversarial Networks

YAO Nai-Ming^{1,2
,},
GUO Qing-Pei^{1,2
,},
QIAO Feng-Chun^{1,2
,},
CHEN Hui^{1,2
, ,},
WANG Hong-An^{1,2,3
,}

1.
Beijing Key Laboratory of Human-Computer Interaction, Institute of Software, Chinese Academy of Sciences, Beijing 100190
2.
University of Chinese Academy of Sciences, Beijing 100049
3.
State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing 100190

Funds:

National Natural Science Foundation of China 61572479

National Fundamental Research Grant of Science and Technology 2017YFB1002805

Frontier Science Key Program of Chinese Academy of Sciences QYZ DY-SSW-JSC041

National Natural Science Foundation of China 61661146002

More Information

Author Bio:
Ph. D. candidate at the Institute of Software, Chinese Academy of Sciences. He received his master degree from Capital Normal University in 2011. His research interest covers areas of affective interaction, machine learning, and computer vision

Master student at the Institute of Software, Chinese Academy of Sciences. He received his bachelor degree from Huazhong University of Science and Technology in 2014. His research interest covers machine learning, computer vision, and affective computing

Master student at the Institute of Software, Chinese Academy of Sciences. He received his bachelor degree from Beijing Forestry University in 2016. His research interest covers areas of deep learning and image synthesis

Professor at the Institute of Software, Chinese Academy of Sciences. He received his Ph. D. degree from the Institute of Software, Chinese Academy of Sciences in 1998. His research interest covers areas of human-computer interaction and real-time intelligence

Corresponding author: CHEN Hui Professor at the Institute of Software, Chinese Academy of Sciences. She received her Ph. D. degree from the School of Computer Science, The Chinese University of Hong Kong in 2006. Her research interest covers areas of human-computer interaction, affective interaction, haptics, and virtual reality. Corresponding author of this paper

摘要

摘要: 人们在自然情感交流中经常伴随着头部旋转和肢体动作，它们往往导致较大范围的人脸遮挡，使得人脸图像损失部分表情信息.现有的表情识别方法大多基于通用的人脸特征和识别算法，未考虑表情和身份的差异，导致对新用户的识别不够鲁棒.本文提出了一种对人脸局部遮挡图像进行用户无关表情识别的方法.该方法包括一个基于Wasserstein生成式对抗网络（Wasserstein generative adversarial net，WGAN）的人脸图像生成网络，能够为图像中的遮挡区域生成上下文一致的补全图像；以及一个表情识别网络，能够通过在表情识别任务和身份识别任务之间建立对抗关系来提取用户无关的表情特征并推断表情类别.实验结果表明，我们的方法在由CK+，Multi-PIE和JAFFE构成的混合数据集上用户无关的平均识别准确率超过了90%.在CK+上用户无关的识别准确率达到了96%，其中4.5%的性能提升得益于本文提出的对抗式表情特征提取方法.此外，在45°头部旋转范围内，本文方法还能够用于提高非正面表情的识别准确率.
- 人脸补全 /
- 用户无关 /
- 人脸表情识别 /
- 生成式对抗网络 /
- 卷积神经网络
Abstract: In natural communication, people would express their expressions with head rotation and body movement, which may result in partial occlusion of face and a consequent information loss regarding facial expression. Also, most of the existing approaches to facial expression recognition are not robust enough to unseen users because they rely on general facial features or algorithms without considering differences between facial expression and facial identity. In this paper, we propose a person-independent recognition method for partially-occluded facial expressions. Based on Wasserstein generative adversarial net (WGAN), a generative network of facial image is trained to perform context-consistent image completion for partially-occluded facial expression images. With an adversarial learning strategy, furthermore, a facial expression recognition network and a facial identity recognition network are established to improve the accuracy and robustness of facial expression recognition via inhibition of intra-class variation. Extensive experimental results demonstrate that 90% average recognition accuracy of facial expression has been reached on a mixed dataset composed of CK+, Multi-PIE, and JAFFE. Moreover, our method achieves 96% accuracy of user-independent recognition on CK+. A 4.5% performance gain is achieved with the novel identity-inhibited expression feature. Our method is also capable of improving recognition accuracy for non-frontal facial expressions within a range of 45-degree head rotation.
- Face completion /
- person-independent /
- facial expression recognition /
- generative adversarial net (GAN) /
- convolutional neural network (CNN)
注释:

1) 本文责任编委左旺孟

HTML全文

图 1 鲁棒人脸表情识别的算法框架

Fig. 1 Framework of our robust facial expression recognition algorithm

下载: 全尺寸图片幻灯片

图 2 人脸补全网络在训练1, 50, 75和100轮之后的输出图像

Fig. 2 Outputs of the proposed face completion networks after training 1, 50, 75, and 100 steps

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图 3 人脸补全网络的训练损失曲线

Fig. 3 Training loss curves of the proposed face completion networks

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图 4 较大范围局部遮挡图像的补全结果

Fig. 4 Image completion results on images with large-scale occlusion

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图 5 不规则局部遮挡图像的补全结果

Fig. 5 Image completion results on images with irregular occlusion

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图 6 在COFW测试集上的补全结果

Fig. 6 Image completion results on images taken from the COFW test set

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图 7 不同方法补全CelebA测试集图像的结果

Fig. 7 Comparisons of different image completion methods on images taken from the CelebA test set

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图 8 人脸表情特征和人脸身份特征的t-SNE可视化

Fig. 8 t-SNE visualization of facial expression features and facial identity features

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图 9 人脸表情特征的t-SNE可视化

Fig. 9 t-SNE visualization of facial expression features

下载: 全尺寸图片幻灯片

表 1 混合数据集中的统一表情分类

Table 1 Unified expression categories in the mixed dataset

CK +	Multi-PIE	JAFFE	统一分类
neutral	neutral	NE	NE (中性)
anger	-	AN	AN (愤怒)
contempt, disgust	squint, disgust	DI	DI (厌恶)
happy	smile	HA	HA (高兴)
sadness	-	SA	SA (悲伤)
surprise	surprise	SU	SU (惊讶)
fear	-	FE	-
-	scream	-	-

下载: 导出CSV

表 2 在混合数据集上的人脸表情识别准确率(%)

Table 2 Results of recognition accuracy (%) on the mixed dataset

数据库名称	设置	NE	AN	DI	HA	SA	SU	平均识别率
混合数据集	PD	95.41	98.85	94.12	96.55	100.00	97.54	97.07
	PI	87.78	95.52	93.15	93.91	82.57	90.82	90.62
CK +	PD	100.00	97.64	100.00	100.00	97.37	98.35	99.05
	PI	98.60	96.43	94.37	100.00	73.40	100.00	92.35
Multi-PIE	PD	87.50	-	91.43	95.45	-	95.35	93.10
	PI	85.00	-	86.36	94.34	-	93.94	90.13
JAFFE	PD	97.87	100.00	98.73	99.13	100.00	97.80	98.84
	PI	90.91	100.00	96.77	100.00	87.14	87.88	93.75

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表 3 混合数据集上人脸表情识别的混淆矩阵

Table 3 Confusion matrix for the mixed dataset

	NE	AN	DI	HA	SA	SU
NE	87.77	1.06	5.32	3.19	0.53	2.13
AN	0	95.52	3.14	0	1.35	0
DI	3.65	0	93.15	0.04	3.16	0
HA	4.78	0.43	0.43	93.91	0.43	0
SA	0.92	11.93	4.59	0	82.57	0
SU	2.55	2.04	2.04	1.02	1.53	90.82

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表 4 在Multi-PIE上针对不同头部姿态和图像补全设置的人脸表情识别准确率(%)

Table 4 Comparisons of recognition accuracy (%) between settings of face completion for different head poses on Multi-PIE

	0	$ \pm 15^\circ$	$\pm 30^\circ$	$\pm 45^\circ$
w/o人脸补全	93.24	88.42	86.85	83.33
w/人脸补全	93.24	91.31	89.70	82.20

下载: 导出CSV

表 5 比较不同方法在CK +表情数据集上的识别准确率

Table 5 Comparisons of average recognition accuracy among different methods on CK +

识别方法	准确率(%)
LBP-TOP^[41]	88.99
MSR^[42]	91.40
3DCNN-DAP^[43]	92.40
DTAN^[44]	91.44
MSCNN^[45]	95.54
GCNET^[46]	97.93
本文方法(w/o类内差异抑制)	91.48
本文方法(w/类内差异抑制)	96.00

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表 6 不同特征和识别方法在CK +上的识别准确率(%)

Table 6 Comparisons of different descriptors and methods on CK + (%)

识别方法	AN	DI	HA	SA	SU	平均
LBP + SRC^[51]	75.56	87.93	98.55	64.29	100.00	85.26
Gabor + SRC^[52]	84.44	98.28	97.10	64.29	98.78	88.58
LPQ + SVM	70.00	65.30	59.31	76.40	88.32	71.87
SIFT + SVM	72.86	56.70	88.75	72.22	96.30	77.37
EFM + SVM	88.37	90.20	87.16	78.38	94.00	87.62

下载: 导出CSV

参考文献(52)

[1]	Zeng Z H, Pantic M, Roisman G I, Huang T S. A survey of affect recognition methods:audio, visual, and spontaneous expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(1):39-58 doi: 10.1109/TPAMI.2008.52
[2]	Valstar M, Pantic M, Patras I. Motion history for facial action detection in video. In: Proceedings of the 2004 IEEE International Conference on Systems, Man, and Cybernetics. The Hague, The Netherlands: IEEE, 2004. 635-640
[3]	Sandbach G, Zafeiriou S, Pantic M, Yin L J. Static and dynamic 3D facial expression recognition:a comprehensive survey. Image and Vision Computing, 2012, 30(10):683-697 doi: 10.1016/j.imavis.2012.06.005
[4]	Gunes H, Schuller B. Categorical and dimensional affect analysis in continuous input:current trends and future directions. Image and Vision Computing, 2013, 31(2):120-136 doi: 10.1016/j.imavis.2012.06.016
[5]	Yang M, Zhang L, Yang J, Zhang D. Robust sparse coding for face recognition. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, USA: IEEE, 2011. 625-632
[6]	Li Y J, Liu S F, Yang J M, Yang M H. Generative face completion. In: Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017. 5892-5900
[7]	Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, USA: ACM, 2012. 1097-1105
[8]	Yu Z D, Zhang C. Image based static facial expression recognition with multiple deep network learning. In: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction. Seattle, USA: ACM, 2015. 435-442
[9]	Kim B K, Dong S Y, Roh J, Kim G, Lee S Y. Fusing aligned and non-aligned face information for automatic affect recognition in the wild: a deep learning approach. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 1499-1508
[10]	Mollahosseini A, Chan D, Mahoor M H. Going deeper in facial expression recognition using deep neural networks. In: Proceedings of the 2016 IEEE Winter Conference on Applications of Computer Vision. Lake Placid, USA: IEEE, 2016. 1-10
[11]	孙晓, 潘汀, 任福继.基于ROI-KNN卷积神经网络的面部表情识别.自动化学报, 2016, 42(6):883-891 http://www.aas.net.cn/CN/abstract/abstract18879.shtml Sun Xiao, Pan Ting, Ren Fu-Ji. Facial expression recognition using ROI-KNN deep convolutional neural networks. Acta Automatica Sinica, 2016, 42(6):883-891 http://www.aas.net.cn/CN/abstract/abstract18879.shtml
[12]	Kan M N, Shan S G, Chang H, Chen X L. Stacked progressive auto-encoders (SPAE) for face recognition across poses. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE, 2014. 1883-1890
[13]	Zhu Z Y, Luo P, Wang X G, Tang X O. Deep learning identity-preserving face space. In: Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, Australia: IEEE, 2013. 113-120
[14]	Yim J, Jung H, Yoo B, Choi C, Park D S, Kim J. Rotating your face using multi-task deep neural network. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE, 2015. 676-684
[15]	Zhang J, Kan M N, Shan S G, Chen X L. Occlusion-free face alignment: deep regression networks coupled with de-corrupt AutoEncoders. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 3428-3437
[16]	Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, et al. Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada: 2014. 2672-2680
[17]	王坤峰, 苟超, 段艳杰, 林懿伦, 郑心湖, 王飞跃.生成式对抗网络GAN的研究进展与展望.自动化学报, 2017, 43(3):321-332 http://www.aas.net.cn/CN/abstract/abstract19012.shtml Wang Kun-Feng, Gou Chao, Duan Yan-Jie, Lin Yi-Lun, Zheng Xin-Hu, Wang Fei-Yue. Generative adversarial networks:the state of the art and beyond. Acta Automatica Sinica, 2017, 43(3):321-332 http://www.aas.net.cn/CN/abstract/abstract19012.shtml
[18]	Wright J, Yang A Y, Ganesh A, Sastry S S, Ma Y. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2):210-227 doi: 10.1109/TPAMI.2008.79
[19]	Zafeiriou S, Petrou M. Sparse representations for facial expressions recognition via l1 optimization. In: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010. 32-39
[20]	Chen H, Li J D, Zhang F J, Li Y, Wang H G. 3D model-based continuous emotion recognition. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE, 2015. 1836-1845
[21]	Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[Online], available: https://arxiv.org/abs/1409.1556, February 11, 2018
[22]	Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, et al. Going deeper with convolutions[Online], available: https://arxiv.org/abs/1409.4842, February 11, 2018
[23]	He K M, Zhang X Y, Ren S Q, Sun J. Deep residual learning for image recognition. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 770-778
[24]	Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[Online], available: https: //arxiv. org/abs/1511. 06434, February 11, 2018
[25]	Arjovsky M, Chintala S, Bottou L. Wasserstein GAN[Online], available: https://arxiv.org/abs/1701.07875, February 11, 2018
[26]	Ding L, Ding X Q, Fang C. Continuous pose normalization for pose-robust face recognition. Signal Processing Letters, 2012, 19(11):721-724 doi: 10.1109/LSP.2012.2215586
[27]	Li S X, Liu X, Chai X J, Zhang H H, Lao S H, Shan S G. Morphable displacement field based image matching for face recognition across pose. In: Proceedings of the 12th European Conference on Computer Vision. Florence, Italy: Springer, 2012. 102-115
[28]	Zhu X Y, Lei Z, Yan J J, Yi D, Li S Z. High-fidelity pose and expression normalization for face recognition in the wild. In: Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE, 2015. 787-796
[29]	Pathak D, Krähenbühl P, Donahue J, Darrell T, Efros A A. Context encoders: feature learning by inpainting. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016. 2536-2544
[30]	Yeh R A, Chen C, Lim T Y, Schwing A G, Hasegawa-Johnson M, Do M N. Semantic image inpainting with deep generative models[Online], available: https://arxiv.org/abs/1607.07539, February 11, 2018
[31]	Lee S H, Plataniotis K N, Ro Y M. Intra-class variation reduction using training expression images for sparse representation based facial expression recognition. IEEE Transactions on Affective Computing, 2014, 5(3):340-351 doi: 10.1109/TAFFC.2014.2346515
[32]	Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd International Conference on Machine Learning. Lille, France: IEEE, 2015. 448-456
[33]	Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. Fort Lauderdale, USA: JMLR, 2011. 315-323
[34]	Hinton G, Srivastava N, Swersky K. Neural networks for machine learning: overview of mini-batch gradient descent[Online], available: http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf, February 11, 2018
[35]	Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S A, et al. ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 2015, 115(3):211-252 doi: 10.1007/s11263-015-0816-y
[36]	Ganin Y, Lempitsky V S. Unsupervised domain adaptation by backpropagation. In: Proceedings of the 32nd International Conference on Machine Learning. Lille, France: IEEE, 2015. 1180-1189
[37]	Liu Z W, Luo P, Wang X G, Tang X O. Deep learning face attributes in the wild. In: Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015. 3730-3738
[38]	Lucey P, Cohn J F, Kanade T, Saragih J, Ambadar Z, Matthews I. The extended Cohn-Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression. In: Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Francisco, USA: IEEE, 2010. 94-101
[39]	Gross R, Matthews I, Cohn J, Kanade T, Baker S. Multi-PIE. Image and Vision Computing, 2010, 28(5):807-813 doi: 10.1016/j.imavis.2009.08.002
[40]	Lyons M, Akamatsu S, Kamachi M, Gyoba J. Coding facial expressions with gabor wavelets. In: Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition. Nara, Japan: IEEE, 1998. 200-205
[41]	Zhao G Y, Pietikäinen M. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(6):915-928 doi: 10.1109/TPAMI.2007.1110
[42]	Ptucha R, Tsagkatakis G, Savakis A. Manifold based sparse representation for robust expression recognition without neutral subtraction. In: Proceedings of the 2011 IEEE International Conference on Computer Vision Workshops. Barcelona, Spain: IEEE, 2011. 2136-2143
[43]	Liu M Y, Li S X, Shan S G, Wang R P, Chen X L. Deeply learning deformable facial action parts model for dynamic expression analysis. In: Proceedings of the 12th Asian Conference on Computer Vision. Singapore, Singapore: Springer, 2014. 143-157
[44]	Jung H, Lee S, Yim J, Park S, Kim J. Joint fine-tuning in deep neural networks for facial expression recognition. In: Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015. 2983-2991
[45]	Zhang K H, Huang Y Z, Du Y, Wang L. Facial expression recognition based on deep evolutional spatial-temporal networks. IEEE Transactions on Image Processing, 2017, 26(9):4193-4203 doi: 10.1109/TIP.2017.2689999
[46]	Kim Y, Yoo B, Kwak Y, Choi C, Kim J. Deep generative-contrastive networks for facial expression recognition[Online], available: https: //arxiv. org/abs/1703. 07140, February 11, 2018
[47]	Burgos-Artizzu X P, Perona P, Dollár P. Robust face landmark estimation under occlusion. In: Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney, Australia: IEEE, 2013. 1513-1520
[48]	Zhang S Q, Zhao X M, Lei B C. Robust facial expression recognition via compressive sensing. Sensors, 2012, 12(3):3747-3761 doi: 10.3390/s120303747
[49]	Ojansivu V, Heikkilä J. Blur insensitive texture classification using local phase quantization. In: Proceedings of the 3rd International Conference on Image and Signal Processing. Cherbourg-Octeville, France: Springer, 2008. 236-243
[50]	Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2):91-110 doi: 10.1023/B:VISI.0000029664.99615.94
[51]	van der Maaten L, Hinton G. Visualizing data using t-SNE. Journal of Machine Learning Research, 2008, 9(11):2579-2605 http://www.mendeley.com/catalog/visualizing-data-using-tsne/
[52]	Huang M W, Wang Z W, Ying Z L. A new method for facial expression recognition based on sparse representation plus LBP. In: Proceedings of the 3rd International Congress on Image and Signal Processing. Yantai, China: IEEE, 2010. 1750-1754