[1]孙文赟,宋昱,陈昌盛.基于卷积-反卷积网络的正交人脸特征学习算法[J].深圳大学学报理工版,2020,37(5):474-481.[doi:10.3724/SP.J.1249.2020.05474]
　SUN Wenyun,SONG Yu,and CHEN Changsheng.An orthogonal facial feature learning method based on convolutional-deconvolutional network[J].Journal of Shenzhen University Science and Engineering,2020,37(5):474-481.[doi:10.3724/SP.J.1249.2020.05474]
点击复制

基于卷积-反卷积网络的正交人脸特征学习算法()
分享到：

《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第37卷
期数:
2020年第5期
页码:
474-481
栏目:
电子与信息科学
出版日期:
2020-09-15

文章信息/Info

Title:
An orthogonal facial feature learning method based on convolutional-deconvolutional network
文章编号:
202005004
作者:
孙文赟; 宋昱; 陈昌盛
深圳市媒体信息内容安全重点实验室，广东省智能信息处理重点实验室，深圳大学电子与信息工程学院，广东深圳518060
Author(s):
SUN Wenyun;  SONG Yu;  and CHEN Changsheng
Shenzhen Key Laboratory of Media Security, Guangdong Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060，Guangdong Province, P.R.China
关键词:
人工智能; 计算机神经网络; 深度学习; 人脸表情识别; 人脸图像分析; 正交人脸特征; 重构损失; 分类损失; 相关性最小损失
Keywords:
artificial intelligence;  computer neural network;  deep learning;  facial expression recognition;  facial image analysis;  orthogonal facial feature;  reconstruction loss;  classification loss;  correlation minimization loss
分类号:
TP311
DOI:
10.3724/SP.J.1249.2020.05474
文献标志码:
A
摘要:
身份特征与表情特征是人脸图像分析中的两组重要特征，传统的有监督正交人脸特征学习(supervised orthogonal facial feature learning, SOFFL)算法虽然能够在给定表情和身份标签时学习这一对特征，但因数据要求较高令其应用受限．提出一种低数据要求的无监督正交人脸特征学习(unsupervised orthogonal facial feature learning, UOFFL)算法，通过提取正交人脸特征的统一框架，假设人脸图像空间中仅有身份和表情变化，使用重构损失、分类损失和相关性最小化损失的组合，采用深度卷积-反卷积神经网络，从已对齐的人脸图像中联合学习，提取身份和表情特征．其中，分类损失用于学习表情特征；相关性最小化损失用于提高身份特征和表情特征之间的独立性；重构损失用于确保两组特征组合的信息完整性．在大规模合成人脸表情数据集(large-scale synthesized facial expression dataset, LSFED)和受限的Radboud人脸数据集(Radboud faces dataset, RaFD)上进行验证，将所学身份特征空间中的欧氏距离用于人脸验证任务，结果表明，算法性能接近联合贝叶斯等有监督人脸识别方法．UOFFL算法可在身份标签缺失的条件下，仅使用表情特征学得身份特征．相比改进前的SOFFL算法， 该方法缓解了对身份标签的依赖， 适用场合更广．
Abstract:
In facial image analysis, the identity and expression are two important features which are related to face recognition and facial expression recognition tasks. The traditional supervised orthogonal facial feature learning method can learn both features jointly once the emotion and identity labels are given. Its application scope is limited by the high data requirement. In this paper, we propose a new method which is a united framework of joint facial feature learning with lower data requirement. It is based on the assumption that there are only two variations in the face space, i.e., the identity and the emotion. By combining reconstruction loss, classification loss, and correlation minimization loss, we use the deep convolutional-deconvolutional neural network to learn and extract identity and expression features from the aligned facial image. The classification loss learns the expression feature. The correlation minimization loss keeps the two features independent of each other. The reconstruction loss confirms the information completeness of the face. When the identity label is missing, the proposed method can learn the identity feature based on the expression labels. By this means, the new method is a kind of unsupervised facial feature learning. It relieves the limitation of the existing method, extends the application scope. The proposed method is evaluated on the large-scale synthesized facial expression dataset and the constrained Radbound face dataset (RaFD) dataset. The Euclid distance in the identity feature space is used for facial recognition task in which the performance of the proposed unsupervised facial feature learning is close to some supervision ones including the joint Bayesian face method. Our method relieves the requirement of identity label in the previous supervised version to get a wider application scope.

参考文献/References:

［1］ SUN Wenyun, ZHAO Haitao, JIN Zhong. A complementary facial representation extracting method based on deep learning［J］. Neurocomputing, 2018, 306(6): 246-259.
［2］ LANGNER O, DOTSCH R, BIJLSTRA G, et al. Presentation and validation of the Radboud faces database［J］. Cognition and Emotion, 2010, 24(8): 1377-1388.
［3］ TRAN L, YIN Xi, LIU Xiaoming. Disentangled representation learning gan for pose-invariant face recognition［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017: 1415-1424.
［4］ ZHANG Zhifei, SONG Yang, QI Hairong. Age progression/regression by conditional adversarial autoencoder［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA: IEEE, 2017: 5810-5818.
［5］ MA Liqian, SUN Qianru, GEORGOULIS S, et al. Disentangled person image generation［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018: 99-108.
［6］ BERTHELOT D, RAFFEL C, ROY A, et al. Understanding and improving interpolation in autoencoders via an adversarial regularizer［EB/OL］. (2018-07-18)［2018-07-23］. https://arxiv.org/abs/1807.07543, 2018.
［7］ ZHU Zhenyao, LUO Ping, WANG Xiaogang, et al. Multi-view perceptron: a deep model for learning face identity and view representations［C］// Proceedings of the 27th Conference on Neural Information Processing Systems. Montreal, Canada: MIT Press, 2014, 1: 217-225.
［8］ DOSOVITSKIY A, SPRINGENBERG J T, BROX T. Learning to generate chairs with convolutional neural networks［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE, 2015: 1538-1546.
［9］ SUN Baochen, FENG Jiashi, SAENKO K. Return of frustratingly easy domain adaptation［C］// Proceedings of the 30th AAAI Conference on Artificial Intelligence. Phoenix, USA: AIII Press, 2016: 2058-2065.
［10］ SUN Baochen, SAENKO K. Deep coral: correlation alignment for deep domain adaptation［C］// Proceedings of the European Conference on Computer Vision.［S. l.］: Spring, 2016: 443-450.
［11］ BOUSMALIS K, TRIGEORGIS G, SILBERMAN N, et al. Domain separation networks［C］// Advances in Neural Information Processing Systems. New York, USA: Curran Associates Inc., 2016: 343-351.
［12］ GATYS L A, ECKER A S, BETHGE M. Image style transfer using convolutional neural networks［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016: 2414-2423.
［13］ ROZANTSEV A, SALZMANN M, FUA P. Beyond sharing weights for deep domain adaptation［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018, 41(4): 801-814.
［14］ LI Yanghao, WANG Naiyan, LIU Jiaying, et al. Demystifying neural style transfer［C］// Proceedings of the International Joint Conference on Artificial Intelligence. Palo Alto, USA: AAAI Press, 2017: 2230-2236.
［15］ LIAO Qianli, LEIBO Joel Z, POGGIO Tomaso. Learning invariant representations and applications to face verification［C］// Advances in Neural Information Processing Systems. Lake Tahoe, USA: MIT Press, 2013: 3057-3065.
［16］ IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift［EB/OL］. (2015-02-11)［2015-03-02］. https://arxiv.org/abs/1502.03167, 2015.
［17］ KAZEMI V, SULLIVAN J. One millisecond face alignment with an ensemble of regression trees［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA: IEEE, 2014: 1867-1874.
［18］ HASSNER T, HAREL S, PAZ E, et al. Effective face frontalization in unconstrained images［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Boston, USA: IEEE, 2015: 4295-4304.
［19］ HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Delving deep into rectifiers: surpassing human-level performance on imageNet classification［C］// Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 1026-1034.
［20］ CHEN Dong, CAO Xudong, WEN Fang, et al. Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification［C］// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Portland, USA: IEEE, 2013: 3025-3032.
［21］ DONAHUE J, JIA Yangqing, VINYALS O, et al. DeCAF: a deep convolutional activation feature for generic visual recognition［C］// Proceedings of the International Conference on Machine Learning. Beijing: JMLR.org, 2014, 32: 647-655.
［22］ CHEN Dong, CAO Xudong, WANG Liwei, et al. Bayesian face revisited: a joint formulation［C］// Proceedings of the 12th European Conference on Computer Vision. Berlin: Springer, 2012: 566-579.

相似文献/References:

[1]潘长城,徐晨,李国.解全局优化问题的差分进化策略[J].深圳大学学报理工版,2008,25(2):211.
　PAN Chang-cheng,XU Chen,and LI Guo.Differential evolutionary strategies for global optimization[J].Journal of Shenzhen University Science and Engineering,2008,25(5):211.
[2]骆剑平,李霞.求解TSP的改进混合蛙跳算法[J].深圳大学学报理工版,2010,27(2):173.
　LUO Jian-ping and LI Xia.Improved shuffled frog leaping algorithm for solving TSP[J].Journal of Shenzhen University Science and Engineering,2010,27(5):173.
[3]蔡良伟,李霞.基于混合蛙跳算法的作业车间调度优化[J].深圳大学学报理工版,2010,27(4):391.
　CAI Liang-wei and LI Xia.Optimization of job shop scheduling based on shuffled frog leaping algorithm[J].Journal of Shenzhen University Science and Engineering,2010,27(5):391.
[4]张重毅,刘彦斌,于繁华,等.CDA市场环境模型进化研究[J].深圳大学学报理工版,2010,27(4):413.
　ZHANG Zhong-yi,LIU Yan-bin,YU Fan-hua,et al.Research on the evolution model of CDA market environment[J].Journal of Shenzhen University Science and Engineering,2010,27(5):413.
[5]姜建国,周佳薇,郑迎春,等.一种双菌群细菌觅食优化算法[J].深圳大学学报理工版,2014,31(1):43.[doi:10.3724/SP.J.1249.2014.01043]
　Jiang Jianguo,Zhou Jiawei,Zheng Yingchun,et al.A double flora bacteria foraging optimization algorithm[J].Journal of Shenzhen University Science and Engineering,2014,31(5):43.[doi:10.3724/SP.J.1249.2014.01043]
[6]蔡良伟,刘思麒,李霞,等.基于蚁群优化的正则表达式分组算法[J].深圳大学学报理工版,2014,31(3):279.[doi:10.3724/SP.J.1249.2014.03279]
　Cai Liangwei,Liu Siqi,Li Xia,et al.Regular expression grouping algorithm based on ant colony optimization[J].Journal of Shenzhen University Science and Engineering,2014,31(5):279.[doi:10.3724/SP.J.1249.2014.03279]
[7]宁剑平,王冰,李洪儒,等.递减步长果蝇优化算法及应用[J].深圳大学学报理工版,2014,31(4):367.[doi:10.3724/SP.J.1249.2014.04367]
　Ning Jianping,Wang Bing,Li Hongru,et al.Research on and application of diminishing step fruit fly optimization algorithm[J].Journal of Shenzhen University Science and Engineering,2014,31(5):367.[doi:10.3724/SP.J.1249.2014.04367]
[8]刘万峰,李霞.车辆路径问题的快速多邻域迭代局部搜索算法[J].深圳大学学报理工版,2015,32(2):196.[doi:10.3724/SP.J.1249.2015.02000]
　Liu Wanfeng,and Li Xia,A fast multi-neighborhood iterated local search algorithm for vehicle routing problem[J].Journal of Shenzhen University Science and Engineering,2015,32(5):196.[doi:10.3724/SP.J.1249.2015.02000]
[9]蔡良伟,程璐,李军,等.基于遗传算法的正则表达式规则分组优化[J].深圳大学学报理工版,2015,32(3):281.[doi:10.3724/SP.J.1249.2015.03281]
　Cai Liangwei,Cheng Lu,Li Jun,et al.Regular expression grouping optimization based on genetic algorithm[J].Journal of Shenzhen University Science and Engineering,2015,32(5):281.[doi:10.3724/SP.J.1249.2015.03281]
[10]陈玉佳,姜波.基于小波神经网络的加工番茄产量预测模型[J].深圳大学学报理工版,2015,32(5):546.[doi:10.3724/SP.J.1249.2015.05546]
　Chen Yujia and Jiang Bo.A wavelet neural network model for processing tomato yield forecasting[J].Journal of Shenzhen University Science and Engineering,2015,32(5):546.[doi:10.3724/SP.J.1249.2015.05546]

备注/Memo

备注/Memo:
Received：2019-12-29；Accepted：2020-02-07
Foundation：National Natural Science Foundation of China (61902250); Natural Science Foundation of Guangdong Province (2020A 1515010563); Shenzhen Science and Technology Resenrch Foundation for Basic Project (JCYJ20180305124550725)
Corresponding author：Lecture CHEN Changsheng. E-mail: cschen@szu.edu.cn
Citation：SUN Wenyun, SONG Yu, CHEN Changsheng. An orthogonal facial feature learning method based on convolutional-deconvolutional network［J］. Journal of Shenzhen University Science and Engineering, 2020, 37(5): 474-481.（in Chinese）
基金项目：国家自然科学基金资助项目(61902250)；广东省自然科学基金资助项目(2020A1515010563)；深圳市科技计划基础研究资助项目(JCYJ20180305124550725)
作者简介：孙文赟(1987—)，深圳大学博士后研究人员．研究方向：深度学习与人脸图像分析．E-mail：wenyunsun@szu.edu.cn
引文：孙文赟，宋昱，陈昌盛．基于卷积-反卷积网络的正交人脸特征学习算法［J］. 深圳大学学报理工版，2020，37(5)：474-481.
更新日期/Last Update: 2020-07-26