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纸质出版日期： 2020-11-16 ，
录用日期： 2020-09-27
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彭小江, 乔宇. 面部表情分析进展和挑战[J]. 中国图象图形学报, 2020,25(11):2337-2348.
Xiaojiang Peng, Yu Qiao. Advances and challenges in facial expression analysis[J]. Journal of Image and Graphics, 2020,25(11):2337-2348.
面部表情分析是计算机通过分析人脸信息尝试理解人类情感的一种技术，目前已成为计算机视觉领域的热点话题。其挑战在于数据标注困难、多人标签一致性差、自然环境下人脸姿态大以及遮挡等。为了推动面部表情分析发展，本文概述了面部表情分析的相关任务、进展、挑战和未来趋势。首先，简述了面部表情分析的几个常见任务、基本算法框架和数据库；其次，对人脸表情识别方法进行了综述，包括传统的特征设计方法以及深度学习方法；接着，对人脸表情识别存在的问题与挑战进行总结思考；最后，讨论了未来发展趋势。通过全面综述和讨论，总结以下观点：1)针对可靠人脸表情数据库规模小的问题，从人脸识别模型进行迁移学习以及利用无标签数据进行半监督学习是两个重要策略；2)受模糊表情、低质量图像以及标注者的主观性影响，非受控自然场景的人脸表情数据的标签库存在一定的不确定性，抑制这些因素可以使得深度网络学习真正的表情特征；3)针对人脸遮挡和大姿态问题，利用局部块进行融合的策略是一个有效的策略，另一个值得考虑的策略是先在大规模人脸识别数据库中学习一个对遮挡和姿态鲁棒的模型，再进行人脸表情识别迁移学习；4)由于基于深度学习的表情识别方法受很多超参数影响，导致当前人脸表情识别方法的可比性不强，不同的表情识别方法有必要在不同的简单基线方法上进行评测。目前，虽然非受控自然环境下的表情分析得到较快发展，但是上述问题和挑战仍然有待解决。人脸表情分析是一个比较实用的任务，未来发展除了要讨论方法的精度也要关注方法的耗时以及存储消耗，也可以考虑用非受控环境下高精度的人脸运动单元检测结果进行表情类别推断。
Facial expression analysis aims to understand human emotions by analyzing visual face information and has been a popular topic in the computer vision community. Its main challenges include annotating difficulties
inconsistent labels
large face poses
and occlusions in the wild. To promote the advance of facial expression analysis
this paper comprehensively reviews recent advances
challenges
and future trends. First
several common tasks for facial expression analysis
basic algorithm pipeline
and datasets are explored. In general
facial expression analysis mainly includes three tasks
namely
facial expression recognition (i.e.
basic emotion recognition)
facial action unit detection
and valence-arousal regression. The well-known pipeline of facial expression analysis mainly consists of three steps
namely
face extraction (which includes face detection
face alignment
and face cropping)
facial feature extraction
and classification (or regression for the valence-arousal task). Datasets for facial expression analysis come from laboratory and real-world environments
and recent datasets mainly focus on in-the-wild conditions. Second
this paper provides an algorithm survey on facial expression recognition
including hand-crafted feature-based methods
deep learning-based methods
and action unit(AU)-based methods. For hand-crafted features
appearance- and geometry-based features can be used. Specifically
traditional appearance-based features mainly include local binary patterns and Gabor features. Geometry-based features are mainly computed from facial key points. In deep learning-based methods
early studies apply deep belief networks while almost all recent methods use deep convolutional neural networks. Apart from direct facial expression recognition
some methods leverage the corresponding map between AUs and emotion categories
and infer categories from detected AUs. Third
this paper summarizes and discusses recent challenges in facial expression recognition (FER)
including the small scale of reliable FER datasets
uncertainties in large-scale FER datasets
occlusion and large pose problems in FER datasets
and comparability of FER algorithms. Lastly
this paper discusses the future trends of facial expression analysis. According to our comprehensive review and discussion
1) for the small-scale challenge posed by reliable FER data
two important strategies are transfer learning from face recognition models and semi-supervised methods based on large-scale unlabeled facial data. 2) Owing to ambiguous expression
low-quality face images
and subjectivity of annotators
uncertain annotations inevitably exist in large-scale in-the-wild FER datasets. For better learning facial expression features
it is beneficial to suppress these uncertain annotations. 3) For the challenges posed by occlusion and large pose
combining different local regions is effective
and another valuable strategy is to first learn an occlusion- and pose-robust face recognition model and then transfer it to facial expression recognition. 4) Current FER methods are difficult to compare due to the impact of many hyper parameters in deep learning methods. Thus
comparing various baselines for different FER methods is necessary. For example
a facial expression recognition method should be compared in the configuration of learning from scratch and from pretrained models. Recently
although facial expression analysis has great progress
the abovementioned challenges remain unsolved. Facial expression analysis is a practical task
and algorithms should also pay attention to the time and memory consumption except for accuracy in the future. In the deep learning era
facial action unit detection in the wild has achieved great progress
and using the results of action unit detection in inferring facial expression categories in the wild may be feasible in the future.
面部表情分析人脸表情识别(FER)卷积神经网络(CNN)深度学习迁移学习
facial expression analysisfacial expression recognition(FER)convolutional neural network(CNN)deep learningtransfer learning
Barrett L F, Adolphs R, Marsella S, Martinez A M and PollakS D. 2019. Emotional expressions reconsidered:challenges to inferring emotion from human facial movements. Psychological Science in the Public Interest, 20(1):1-68[DOI:10.1177/1529100619832930]
Barsoum E, Zhang C, Ferrer C C and Zhang Z Y. 2016. Training deep networks for facial expression recognition with crowd-sourced label distribution//Proceedings of the 18th ACM International Conference on Multimodal Interaction. Tokyo: ACM: 279-283[DOI: 10.1145/2993148.2993165http://dx.doi.org/10.1145/2993148.2993165]
Bashyal S and Venayagamoorthy G K. 2008. Recognition of facial expressions using Gabor wavelets and learning vector quantization. Engineering Applications of Artificial Intelligence, 21(7):1056-1064[DOI:10.1016/j.engappai.2007.11.010]
Benitez-Quiroz C F, Srinivasan R and Martinez A M. 2016. EmotioNet: an accurate, real-time algorithm for the automatic annotation of a million facial expressions in the wild//Proceedings of 2016 IEEEConference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE: 5562-5570[DOI: 10.1109/CVPR.2016.600http://dx.doi.org/10.1109/CVPR.2016.600]
Cai J, Meng Z B, Khan A S, Li Z Y, O'Reilly J and Tong Y. 2018. Island loss for learning discriminative features in facial expression recognition//Proceedings of the 13th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2018). Xi'an: IEEE: 302-309[DOI: 10.1109/FG.2018.00051http://dx.doi.org/10.1109/FG.2018.00051]
Corneanu C A, Simón M O, Cohn J F and Guerrero S E. 2016. Survey on RGB, 3D, thermal, and multimodal approaches for facial expression recognition:history, trends, and affect-related applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(8):1548-1568[DOI:10.1109/TPAMI.2016.2515606]
Dalal N and Triggs B. 2005. Histograms of oriented gradients for human detection//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego: IEEE: 886-893[DOI: 10.1109/CVPR.2005.177http://dx.doi.org/10.1109/CVPR.2005.177]
Dapogny A, Bailly K and Dubuisson S. 2015. Dynamic facial expression recognition by joint static and multi-time gap transition classification//Proceedings of the 11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). Ljubljana: IEEE: 1-6[DOI: 10.1109/FG.2015.7163111http://dx.doi.org/10.1109/FG.2015.7163111]
Darwin C. 1872. The Expression of the Emotions in Man and Animals. London: John Murray
Dhall A, Asthana A, Goecke R and Gedeon T. 2011. Emotion recognition using PHOG and LPQ features//Proceedings of the 9th IEEE International Conference on Automatic Face and Gesture Recognition (FG11). Santa Barbara: IEEE: 878-883[DOI: 10.1109/FG.2011.5771366http://dx.doi.org/10.1109/FG.2011.5771366]
Dhall A, Kaur A, Goecke R and Gedeon T. 2018. EmotiW 2018: audio-video, student engagement and group-level affect prediction//Proceedings of the 20th ACM International Conference on Multimodal Interaction. Boulder: ACM: 653-656[DOI: 10.1145/3242969.3264993http://dx.doi.org/10.1145/3242969.3264993]
Ding H, Zhou S H and Chellappa R. 2017. FaceNet2ExpNet: regularizing a deep face recognition net for expression recognition//Proceedings of the 12th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2017). Washington: IEEE: 118-126[DOI: 10.1109/FG.2017.23http://dx.doi.org/10.1109/FG.2017.23]
Ding H, Zhou P and Chellappa R. 2020. Occlusion-Adaptive Deep Network for Robust Facial Expression Recognition[EB/OL].[2020-05-12].https://arxiv.org/pdf/2005.06040.pdfhttps://arxiv.org/pdf/2005.06040.pdf
Ekman P and Friesen W V. 1971. Constants across cultures in the face and emotion. Journal of Personality and Social Psychology, 17(2):124-129[DOI:10.1037/h0030377]
Ekman P and Friesen W V. 1978. Facial Action Coding System: A Technique for the Measurement of Facial Movement. Palo Alto: Consulting Psychologists Press
Ekman P and Heider K G. 1988. The universality of a contempt expression:a replication. Motivation and Emotion, 12(3):303-308[DOI:10.1007/bf00993116]
Fan Y, Lu X J, Li D and Liu Y L. 2016. Video-based emotion recognition using CNN-RNN and C3D hybrid networks//Proceedings of the 18th ACM International Conference on Multimodal Interaction. Tokyo: ACM: 445-450[DOI: 10.1145/2993148.2997632http://dx.doi.org/10.1145/2993148.2997632]
Feldman L A. 1995. Valence focus and arousal focus:individual differences in the structure of affective experience. Journal of Personality and Social Psychology, 69(1):153-166[DOI:10.1037/0022-3514.69.1.153]
Friesen W V and Ekman P. 1983. Emfacs-7: Emotional Facial Action Coding System. San Francisco: University of California
Goodfellow I J, Erhan D, Carrier P L, Courville A, Mirza M, Hamner B, Cukierski W, Tang Y C, Thaler D, Lee D H, Zhou Y B, Ramaiah C, Feng F X, Li R F, Wang X J, Athanasakis D, Shawe-Taylor J, Milakov M, Park J, Ionescu R, Popescu M, Grozea C, Bergstra J, Xie J J, Romaszko L, Xu B, Chuang Z and Bengio Y. 2013. Challenges in representation learning: a report on three machine learning contests//Proceedings of the 20th International Conference on Neural Information Processing. Daegu: Springer: 117-124[DOI: 10.1007/978-3-642-42051-1_16http://dx.doi.org/10.1007/978-3-642-42051-1_16]
Kahou S E, Pal C J, Bouthillier X, Froumenty P, GülçehreÇ, Memisevic R, Vincent P, Courville A, Bengio Y, Ferrari R C, Mirza M, Jean S, Carrier P L, Dauphin Y, Boulanger-Lewandowski N, Aggarwal A, Zumer J, Lamblin P, Raymond J P, Desjardins G, Pascanu R, Warde-Farley D, Torabi A, Sharma A, Bengio E, CôtéM, Konda K R, and Wu Z Z. 2013. Combining modality specific deep neural networks for emotion recognition in video//Proceedings of the 15th ACM on International Conference on Multimodal Interaction. Sydney: ACM: 543-550[DOI: 10.1145/2522848.2531745http://dx.doi.org/10.1145/2522848.2531745]
Kanade T, Cohn J F and Tian Y L. 2000. Comprehensive database for facial expression analysis//Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition. Grenoble: IEEE: 46-53[DOI: 10.1109/AFGR.2000.840611http://dx.doi.org/10.1109/AFGR.2000.840611]
Khorrami P, Le Paine T and Huang T S. 2015. Do deep neural networks learn facial action units when doing expression recognition?//Proceedings of 2015 IEEE International Conference on Computer Vision Workshop (ICCVW). Santiago: IEEE: 19-27[DOI: 10.1109/ICCVW.2015.12http://dx.doi.org/10.1109/ICCVW.2015.12]
Knyazev B, Shvetsov R, Efremova N and Kuharenko A. 2017. Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video[EB/OL].[2020-05-19].https://arxiv.org/pdf/1711.04598.pdfhttps://arxiv.org/pdf/1711.04598.pdf
Kossaifi J, Tzimiropoulos G, Todorovic S and Pantic M. 2017. AFEW-VA database for valence and arousal estimation in-the-wild. Image and Vision Computing, 65:23-36[DOI:10.1016/j.imavis.2017.02.001]
Kotsia I and Pitas I. 2007. Facial expression recognition in image sequences using geometric deformation features and support vector machines. IEEE Transactions on Image Processing, 16(1):172-187[DOI:10.1109/TIP.2006.884954]
Kotsia I, Buciu I and Pitas I. 2008. An analysis of facial expression recognition under partial facial image occlusion. Image and Vision Computing, 26(7):1052-1067[DOI:10.1016/j.imavis.2007.11.004]
Krizhevsky A, Sutskever I and Hinton G E. 2012. ImageNet classification with deep convolutional neural networks//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe: ACM: 1097-1105.
Li S and Deng W H. 2019. Blended emotion in-the-wild:multi-label facial expression recognition using crowdsourced annotations and deep locality feature learning. International Journal of Computer Vision, 127(6/7):884-906[DOI:10.1007/s11263-018-1131-1]
Li S and Deng W H. 2020. Deep facial expression recognition: a survey. IEEE Transactions on Affective Computing: #9039580[DOI: 10.1109/TAFFC.2020.2981446http://dx.doi.org/10.1109/TAFFC.2020.2981446]
Li S, Deng W H and Du J P. 2017. Reliable crowdsourcing and deep locality-preserving learning for expression recognition in the wild//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE: 2584-2593[DOI: 10.1109/CVPR.2017.277http://dx.doi.org/10.1109/CVPR.2017.277]
Li Y, Zeng J B, Shan S G and Chen X L. 2019. Occlusion aware facial expression recognition using CNN with attention mechanism. IEEE Transactions on Image Processing, 28(5):2439-2450[DOI:10.1109/TIP.2018.2886767]
Li Z S, Imai J I and Kaneko M. 2009. Facial-component-based bag of words and PHOG descriptor for facial expression recognition//Proceedings of 2009 IEEE International Conference on Systems, Man and Cybernetics. San Antonio: IEEE: 1353-1358[DOI: 10.1109/ICSMC.2009.5346254http://dx.doi.org/10.1109/ICSMC.2009.5346254]
Liu M Y, Li S X, Shan S G and Chen X L. 2013. AU-aware deep networks for facial expression recognition//Proceedings of the 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition. Shanghai: IEEE: 1-6[DOI: 10.1109/FG.2013.6553734http://dx.doi.org/10.1109/FG.2013.6553734]
Liu P, Han S Z, Meng Z B and Tong Y. 2014. Facial expression recognition via a boosted deep belief network///Proceedings of 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus: IEEE: 1805-1812[DOI: 10.1109/CVPR.2014.233http://dx.doi.org/10.1109/CVPR.2014.233]
Liu P, Wei Y C, Meng Z B, Deng W H, Zhou J T and Yang Y. 2020. Omni-supervised facial expression recognition: a simple baseline[EB/OL].[2020-05-18].https://arxiv.org/pdf/2005.08551.pdfhttps://arxiv.org/pdf/2005.08551.pdf
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C Y and Berg A C. 2016. SSD: single shot multibox detector//Proceedings of the 14th European Conference on Computer Vision. Amsterdam: Springer: 21-37[DOI: 10.1007/978-3-319-46448-0_2http://dx.doi.org/10.1007/978-3-319-46448-0_2]
Lucey P, Cohn J F, Kanade T, Saragih J, Ambadar Z and Matthews I. 2010. The Extended Cohn-Kanade dataset (CK+): a complete dataset for action unit and emotion-specified expression//Proceedings of 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops. San Francisco: IEEE: 94-101[DOI: 10.1109/CVPRW.2010.5543262http://dx.doi.org/10.1109/CVPRW.2010.5543262]
Lyons M, Akamatsu S, Kamachi M and Gyoba J. 1998. Coding facial expressions with Gabor wavelets//Proceedings of the 3rd IEEE International Conference on Automatic Face and Gesture Recognition. Nara: IEEE: 200-205[DOI: 10.1109/AFGR.1998.670949http://dx.doi.org/10.1109/AFGR.1998.670949]
Mavadati S M, Mahoor M H, Bartlett K, Trinh P and Cohn J F. 2013. DISFA:a spontaneous facial action intensity database. IEEE Transactions on Affective Computing, 4(2):151-160[DOI:10.1109/T-AFFC.2013.4]
McDuff D, Mahmoud A, Mavadati M, Amr M, Turcot J and el Kaliouby R. 2016. AFFDEX SDK: a cross-platform real-time multi-face expression recognition toolkit//Proceedings of 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. San Jose: ACM: 3723-3726[DOI: 10.1145/2851581.2890247http://dx.doi.org/10.1145/2851581.2890247]
McKeown G, Valstar M, Cowie R, Pantic M and Schr der M. 2012. The SEMAINE database:annotated multimodal records of emotionally colored conversations between a person and a limited agent. IEEE Transactions on Affective Computing, 3(1):5-17[DOI:10.1109/T-AFFC.2011.20]
Meng D B, Peng X J, Wang K and Qiao Y. 2019. Frame attention networks for facial expression recognition in videos//Proceedings of 2019 IEEE International Conference on Image Processing. Taipei, China: IEEE: 3866-3870[DOI: 10.1109/ICIP.2019.8803603http://dx.doi.org/10.1109/ICIP.2019.8803603]
Minsky M. 1986. The Society of Mind. New York: Simon and Schuster
Minsky M. 2006. The Emotion Machine. New York: Pantheon
Mollahosseini A, Chan D and Mahoor M H. 2016. Going deeper in facial expression recognition using deep neural networks//Proceedings of 2016 IEEE Winter Conference on Applications of Computer Vision (WACV). Lake Placid: IEEE: 1-10[DOI: 10.1109/WACV.2016.7477450http://dx.doi.org/10.1109/WACV.2016.7477450]
Mollahosseini A, Hasani B and Mahoor M H. 2019. AffectNet:a database for facial expression, valence, and arousal computing in the wild. IEEE Transactions on Affective Computing, 10(1):18-31[DOI:10.1109/TAFFC.2017.2740923]
Pantic M and Patras I. 2006. Dynamics of facial expression:recognition of facial actions and their temporal segments from face profile image sequences. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 36(2):433-449[DOI:10.1109/TSMCB.2005.859075]
Pantic M, Valstar M, Rademaker R and Maat L. 2005. Web-based database for facial expression analysis//Proceedings of 2005 IEEE International Conference on Multimedia and Expo. Amsterdam: IEEE: #5[DOI: 10.1109/ICME.2005.1521424http://dx.doi.org/10.1109/ICME.2005.1521424]
Ramanathan S, Kassim A, Venkatesh Y V and Wah W S. 2006. Human facial expression recognition using a 3D morphable model//Proceedings of 2006 International Conference on Image Processing. Atlanta: IEEE: 661-664[DOI: 10.1109/ICIP.2006.312417http://dx.doi.org/10.1109/ICIP.2006.312417]
Ranzato M A, Susskind J, Mnih V and Hinton G. 2011. On deep generative models with applications to recognition//Proceedings of 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Providence: IEEE: 2857-2864[DOI: 10.1109/CVPR.2011.5995710http://dx.doi.org/10.1109/CVPR.2011.5995710]
Redmon J, Divvala S, Girshick R and Farhadi A. 2016. You only look once: unified, real-time object detection//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE: 779-788[DOI: 10.1109/CVPR.2016.91http://dx.doi.org/10.1109/CVPR.2016.91]
Ruiz A, Van De Weijer J and Binefa X. 2015. From emotions to action units with hidden and semi-hidden-task learning//Proceedings of 2015 IEEE International Conference on Computer Vision (ICCV). Santiago: IEEE: 3703-3711[DOI: 10.1109/ICCV.2015.422http://dx.doi.org/10.1109/ICCV.2015.422]
Russell J A. 1980. A circumplex model of affect. Journal of Personality and Social Psychology, 39(6):1161-1178[DOI:10.1037/h0077714]
Shan C F, Gong S G and McOwan P W. 2009. Facial expression recognition based on local binary patterns:a comprehensive study. Image and Vision Computing, 27(6):803-816[DOI:10.1016/j.imavis.2008.08.005]
Suwa M, Sugie N and Fujimora K. 1978. A preliminary note on pattern recognition of human emotional expression//Proceedings of the 4th International Joint Conference on Pattern Recognition. Kyoto: IEEE: 408-410
Taheri S, Qiang Q and Chellappa R. 2014. Structure-preserving sparse decomposition for facial expression analysis. IEEE Transactions on Image Processing, 23(8):3590-3603[DOI:10.1109/TIP.2014.2331141]
Tang H and Huang T S. 2008.3D facial expression recognition based on properties of line segments connecting facial feature points//Proceedings of the 8th IEEE International Conference on Automatic Face and Gesture Recognition. Amsterdam: IEEE: 1-6[DOI: 10.1109/AFGR.2008.4813304http://dx.doi.org/10.1109/AFGR.2008.4813304]
Tang Y C. 2013. Deep learning using linear support vector machines[EB/OL].[2015-02-21].https://arxiv.org/pdf/1306.0239.pdfhttps://arxiv.org/pdf/1306.0239.pdf
Velusamy S, Kannan H, Anand B, Sharma A and Navathe B. 2011. A method to infer emotions from facial action units//Proceedings of 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Prague: IEEE: 2028-2031[DOI: 10.1109/ICASSP.2011.5946910http://dx.doi.org/10.1109/ICASSP.2011.5946910]
Viola P and Jones M J. 2004. Robust real-time face detection. International Journal of Computer Vision, 57(2):137-154[DOI:10.1023/B:VISI.0000013087.49260.fb]
Wang K, Peng X J, Yang J F, Lu S J and Qiao Y. 2020b. Suppressing uncertainties for large-scale facial expression recognition[EB/OL].[2020-05-12].https://arxiv.org/pdf/2002.10392.pdfhttps://arxiv.org/pdf/2002.10392.pdf[DOI: 10.1109/CVPR42600.2020.00693http://dx.doi.org/10.1109/CVPR42600.2020.00693].
Wang K, Peng X J, Yang J F, Meng D B and Qiao Y. 2020a. Region attention networks for pose and occlusion robust facial expression recognition. IEEE Transactions on Image Processing, 29:4057-4069[DOI:10.1109/TIP.2019.2956143]
Wang S M, Shuai H and Liu Q S. 2020. Facial expression recognition based on deep facial landmark features. Journal of Image and Graphics, 25(4):813-823
王善敏, 帅惠, 刘青山. 2020.关键点深度特征驱动人脸表情识别.中国图象图形学报, 25(4):813-823[DOI:10.11834/jig.190331]
Yang H Y, Ciftci U and Yin L J. 2018. Facial expression recognition by de-expression residue learning//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake: IEEE: 2168-2177[DOI: 10.1109/CVPR.2018.00231http://dx.doi.org/10.1109/CVPR.2018.00231]
Zeng J B, Shan S G and Chen X L. 2018. Facial expression recognition with inconsistently annotated datasets//Proceedings of the 15th European Conference on Computer Vision. Munich: Springer: 222-237[DOI: 10.1007/978-3-030-01261-8_14http://dx.doi.org/10.1007/978-3-030-01261-8_14]
Zhang K P, Zhang Z P, Li Z F and Qiao Y. 2016. Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23(10):1499-1503[DOI:10.1109/LSP.2016.2603342]
Zhang X, Yin L J, Cohn J F, Canavan S, Reale M, Horowitz A, Liu P and Girard J M. 2014. BP4D-Spontaneous:a high resolution spontaneous 3D dynamic facial expression database. Image and Vision Computing, 32(10):692-706[DOI:10.1016/j.imavis.2014.06.002]
Zhao G Y and Pietikainen M. 2007. Dynamic texture recognition using local binary patterns with an application to facial expressions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(6):915-928[DOI:10.1109/TPAMI.2007.1110]
Zhao G Y, Huang X H, Taini M, Li S Z and Pietikäinen M. 2011. Facial expression recognition from near-infrared videos. Image and Vision Computing, 29(9):607-619[DOI:10.1016/j.imavis.2011.07.002]
Zhi R C, Liu M Y and Zhang D Z. 2020. A comprehensive survey on automatic facial action unit analysis. The Visual Computer, 36(5):1067-1093[DOI:10.1007/s00371-019-01707-5]
Zhou H S, Meng D B, Zhang Y Y, Peng X J, Du J, Wang K and Qiao Y. 2019. Exploring emotion features and fusion strategies for audio-video emotion recognition//Proceedings of ACM International Conference on Multimodal Interaction. Suzhou: ACM: 562-566[DOI: 10.1145/3340555.3355713http://dx.doi.org/10.1145/3340555.3355713]
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