[1]MO Hongwei,FU Zhijie.Unsupervised cross-domain expression recognition based on transfer learning[J].CAAI Transactions on Intelligent Systems,2021,16(3):397-406.[doi:10.11992/tis.202008034]
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Unsupervised cross-domain expression recognition based on transfer learning

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 16 Number of periods: 2021 3 Page number: 397-406 Column: 学术论文—机器学习 Public date: 2021-05-05
Title:
Unsupervised cross-domain expression recognition based on transfer learning
Author(s):
MO Hongwei;  FU Zhijie
Automation College, Harbin Engineering University, Harbin 150001, China
Keywords:
expression recognition; unsupervised; cross-domain; transfer learning; feature transformation; joint alignment; public subspace; domain adaptive
CLC:
TP181
DOI:
10.11992/tis.202008034
Abstract:
This paper primarily studies unsupervised cross-domain facial expression recognition based on transfer learning. In recent years, many proposed methods have achieved satisfactory results in facial expression recognition. However, these methods usually assume that the training and test data come from the same data set and therefore have the same distribution. In practical applications, this assumption is usually untrue, especially when the training and test sets come from different data sets, also known as the cross-domain facial expression recognition problem. To solve this problem, we propose a migration learning method (domain align learning) based on joint distributed alignment for cross-domain facial expression recognition. By determining a feature transform, the source and target domain data are mapped onto a common subspace, wherein edge distribution and conditional distribution are aligned jointly to reduce the distribution difference between domains, and then a domain adaptive classifier is obtained by training the transformed features to predict the target domain sample label. To verify the effectiveness of the proposed algorithm, many experiments are performed on three databases, CK+, Oulu-CASIA NIR, and Oulu-CASIA VIS. The experimental results show the effectiveness of the proposed algorithm in cross-domain facial expression recognition.
References:
[1] ZHENG Wenming, TANG Hao, HUANG T S. Emotion recognition from non-frontal facial images[M]//KONAR A, CHAKRABORTY A. Emotion Recognition:A Pattern Analysis Approach. Hoboken, USA:John Wiley & Sons, 2015:183-213.
[2] WANG Yongjin, GUAN Ling, VENETSANOPOULOS A N. Kernel cross-modal factor analysis for information fusion with application to bimodal emotion recognition[J]. IEEE transactions on multimedia, 2012, 14(3):597-607.
[3] YAN Jingjie, ZHENG Wenming, XU Guanming, et al. Sparse kernel reduced-rank regression for bimodal emotion recognition from facial expression and speech[J]. IEEE transactions on multimedia, 2016, 18(7):1319-1329.
[4] ZENG Zhihong, PANTIC M, ROISMAN G I, et al. A survey of affect recognition methods:audio, visual, and spontaneous expressions[J]. IEEE transactions on pattern analysis and machine intelligence, 2009, 31(1):39-58.
[5] CHU W S, DE LA TORRE F, COHN J F. Selective transfer machine for personalized facial expression analysis[J]. IEEE transactions on pattern analysis and machine intelligence, 2017, 39(3):529-545.
[6] ZHENG Wenming, ZHOU Xiaoyan, ZOU Cairong, et al. Facial expression recognition using kernel canonical correlation analysis (KCCA)[J]. IEEE transactions on neural networks, 2006, 17(1):233-238.
[7] UDDIN M Z. A local feature-based facial expression recognition system from depth video[M]//DELIGIANNIDIS L, ARABNIA H R. Emerging Trends in Image Processing, Computer Vision and Pattern Recognition. Amsterdam:Elsevier, 2015:407-419.
[8] YU Zhiding, ZHANG Cha. Image based static facial expression recognition with multiple deep network learning[C]//Proceedings of the 2015 ACM on International Conference on Multimodal Interaction. New York, USA:ACM, 2015:433?442.
[9] BACCOUCHE M, MAMALET F, WOLF C, et al. Spatio-temporal convolutional sparse auto-encoder for sequence classification[C]//Proceedings of the of British Machine Vision Conference. Guildford, UK:BMVA Press, 2012:12.
[10] WANG Hua, NIE Feiping, HUANG Heng, et al. Dyadic transfer learning for cross-domain image classification[C]//IEEE International Conference on Computer Vision. Barcelona, Spain:IEEE, 2011:551?556.
[11] LUO Jie, TOMMASI T, CAPUTO B. Multiclass transfer learning from unconstrained priors[C]//IEEE International Conference on Computer Vision. Barcelona, Spain:IEEE, 2011:1863?1870.
[12] ROY S D, MEI Tao, ZENG Wenjun, et al. SocialTransfer:cross-domain transfer learning from social streams for media applications[C]//Proceedings of the 20th ACM international conference on Multimedia. Nara, Japan:ACM, 2012:649?658.
[13] WANG Shuhui, JIANG Shuqiang, HUANG Qingming, et al. Multi-feature metric learning with knowledge transfer among semantics and social tagging[C]//Proceedings of 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence:IEEE, 2012:2240?2247.
[14] AYTAR Y, ZISSERMAN A. Tabula rasa:model transfer for object category detection[C]//Proceedings of 2011 International Conference on Computer Vision. Barcelona, Spain:IEEE, 2011:2252?2259.
[15] GOPALAN R, LI Ruonan, CHELLAPPA R. Domain adaptation for object recognition:an unsupervised approach[C]//2011 International Conference on Computer Vision. Barcelona, Spain:IEEE, 2011:999?1006.
[16] GUILLAUMIN M, FERRARI V. Large-scale knowledge transfer for object localization in ImageNet[C]//Proceedings of 2012 IEEE Computer Vision and Pattern Recognition. Providence, USA:IEEE, 2012:3202?3209.
[17] LAMPERT C H, NICKISCH H, HARMELING S. Learning to detect unseen object classes by between-class attribute transfer[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, USA:IEEE, 2009:951?958.
[18] JHUO I H, LIU D, LEE D T, et al. Robust visual domain adaptation with low-rank reconstruction[C]//2012 IEEE Computer Vision and Pattern Recognition. Providence, USA:IEEE, 2012:2168?2175.
[19] LAMPERT C H, KR?MER O. Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning[C]//Proceedings of the 11th European Conference on Computer Vision. Heraklion, Crete, Greece:Springer-Verlag, 2010:566?579.
[20] QIU Qiang, PATEL V M, TURAGA P, et al. Domain adaptive dictionary learning[C]//Proceedings of the 12th European Conference on Computer Vision. Florence, Italy:Springer-Verlag, 2012:631?645.
[21] DAI Wenyuan, YANG Qiang, XUE Guirong, et al. Boosting for transfer learning[C]//Proceedings of the 24th International Conference on Machine Learning. Corvalis, USA:ACM, 2007:193?200.
[22] PAN S J, TSANG I W, KWOK J T, et al. Domain adaptation via transfer component analysis[J]. IEEE transactions on neural networks, 2011, 22(2):199-210.
[23] GONG Boqing, SHI Yuan, SHA Fei, et al. Geodesic flow kernel for unsupervised domain adaptation[C]//Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Providence, USA:IEEE, 2012:2066?2073.
[24] ZHONG Erheng, FAN Wei, PENG Jing, et al. Cross domain distribution adaptation via kernel mapping[C]//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris, France:ACM, 2009.
[25] BRUZZONE L, MARCONCINI M. Domain adaptation problems:A DASVM classification technique and a circular validation strategy[J]. IEEE transactions on pattern analysis and machine intelligence, 2010, 32(5):770-787.
[26] QUANZ B, HUAN J, MISHRA M. Knowledge transfer with low-quality data:A feature extraction issue[J]. IEEE transactions on knowledge and data engineering, 2012, 24(10):1789-1802.
[27] LUCEY P, COHN J F, KANADE T, et al. The extended Cohn-Kanade dataset (CK+):a complete dataset for action unit and emotion-specified expression[C]//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops. San Francisco, USA:IEEE, 2010:94-101.
[28] KANADE T, COHN J F, TIAN Yingli. Comprehensive database for facial expression analysis[C]//Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition. Grenoble, France:IEEE, 2020:46-53.
[29] ZHAO Guoying, HUANG Xiaohua, TAINI M, et al. Facial expression recognition from near-infrared videos[J]. Image and vision computing, 2011, 29(9):607-619.
[30] HUANG J, GRETTON A, BORGWARDT K, et al. Correcting sample selection bias by unlabeled data[J]. Advances in neural information processing systems, 2006, 19:601-608.
[31] SUGIYAMA M, NAKAJIMA S, KASHIMA H, et al. Direct importance estimation with model selection and its application to covariate shift adaptation[C]//Proceedings of 21st Annual Conference on Advances in Neural Information Processing Systems. Vancouver, Canada:NIPS, 2008:1433-1440.
[32] 刘斌, 米强, 徐岩. LBP和MB-LBP加权融合的人脸识别[J]. 计算机工程与设计, 2018, 39(2):551-556
LIU Bin, MI Qiang, XU Yan. Face recognition based on weighted fusion of LBP and MB-LBP features[J]. Computer engineering and design, 2018, 39(2):551-556
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