表情识别数据

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转自（http://blog.csdn.net/computerme/article/details/49469767）

CK and CK+
It contains 97 subjects, which posed in a lab situation for the six universal expressions and the neutral expression. Its extension CK+ contains 123 subjects but the new videos were shot in a similar environment.
Reference: P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, “The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops CVPR4HB’10, 2010, pp. 94–101.
Website: http://www.pitt.edu/~emotion/ck-spread.htm
Modalities: Visual
说明: ck只有静态图片，CK+包括视频。表情标签为分类离散值。
JAFFE
It contains 219 images of 10 Japanese females. However, it has a limited number of samples, subjects and has been created in a lab controlled environment.
Website: http://www.kasrl.org/jaffe.html
Modalities: visual
说明: 只有219张表情图片。表情标签为分类离散值。
HUMAINE Database
Datafiles containing emotion labels, gesture labels, speech labels and FAPS all readable in ANVI（标签等信息要用ANVI工具才能打开）
Modalities: Audio+visual + gesture
Website: http://emotion-research.net/download/pilot-db/
说明: 下载数据集后里面只有视频，没有标签等信息。
Recola database
Totally 34 subjects; 14 male, 20 female
Reference: FABIEN R., ANDREAS S., JUERGEN S., DENIS L.. Introducing the RECOLA multimodal corpus of collaborative and affective interactions[C]//10th IEEE Int’l conf. and workshops on automatic face and gesture recognition. Shanghai, CN: IEEE Press, 2013:1-8.
Website: http://diuf.unifr.ch/diva/recola/index.html
Modalities: Audio+visual+ EDA, ECG（生理模态）
说明: 数据集共34个视频，表情标签为Arousal-Valence的连续值。标签存在csv文件里。
MMI
The database consists of over 2900 videos and high-resolution still images of 75 subjects. It is fully annotated for the presence of AUs in videos (event coding), and partially coded on frame-level, indicating for each frame whether an AU is in either the neutral, onset, apex or offset phase. A small part was annotated for audio-visual laughters. The database is freely available to the scientific community.
Reference:
a) Induced Disgust, Happiness and Surprise: an Addition to the MMI Facial Expression Database
M. F. Valstar, M. Pantic. Proceedings of Int’l Conf. Language Resources and Evaluation, Workshop on EMOTION. Malta, pp. 65 - 70, May 2010.
b) Web-based database for facial expression analysis,M. Pantic, M. F. Valstar, R. Rademaker, L. Maat. Proceedings of IEEE Int’l Conf. Multimedia and Expo (ICME’05). Amsterdam, The Netherlands, pp. 317 - 321, July 2005.
Modalities: visual(视频)
Website: http://mmifacedb.eu/
　　　　 http://ibug.doc.ic.ac.uk/research/mmi-database/
说明: 该数据集很大，全部包括2900个视频，标签主要是AU的标签，标签存在xml文件里。
NVIE 中科大采集的一个数据集
中科大NVIE数据集包括自发表情库和人为表情库，本实验采用其中的自发表情库。自发表情库是通过特定视频诱发并在三种光照下（正面、左侧、右侧光照）采集的表情库，其中正面光照103人，左侧光照99人，右侧光照103人。每种光照下，每人有六种表情（喜悦、愤怒、哀伤、恐惧、厌恶、惊奇）中的三种以上，每种表情的平静帧以及最大帧都已挑出
Reference: WANG Shangfei, LIU Zhilei, LV Siliang, LV Yanpeng, et al. A Natural Visible and Infrared Facial Expression Database for Expression Recognition and Emotion Inference[J]. IEEE Transactions on Multimedia, 2010, 12(7): 682-691.
Website: http://nvie.ustc.edu.cn/
Modalities: visual(图片)
说明: 标签以Excel文件给出，标签包括表情类的强度，如disgust的表情强度。标签还包括Arousal-Valence标签。
RU-FACS database
This database consists of spontaneous facial expressions from multiple views, with ground truth FACS codes provided by two facial expression experts.
We have collected data from 100 subjects, 2.5 minutes each. This database constitutes a significant contribution towards the 400-800 minute database recommended in the feasibility study for fully automating FACS. To date we have human FACS coded the upper faces of 20% the subjects.
Reference: M. S. Bartlett, G. Littlewort, M. G. Frank, C. Lainscsek, I. R. Fasel, and J. R. Movellan, “Automatic recognition of facial actions in spontaneous expressions,” Journal of Multimedia, vol. 1, no. 6, pp. 22–35, 2006. 3, 5
Website: http://mplab.ucsd.edu/grants/project1/research/rufacs1-dataset.html
说明: 该数据集的标签是FACS编码的标签（只有部分视频才有标签），目前该数据集还未向研究者公开。
Belfast naturalistic database
The Belfast database consists of a combination of studio recordings and TV programme grabs labelled with particular expressions. The number of TV clips in this database is sparse
Modalities: Audio-visual(视频)
Reference: E. Douglas-Cowie, R. Cowie, and M. Schr¨oder, “A New Emotion Database: Considerations, Sources and Scope,” in ISCAITRW on Speech and Emotion, 2000, pp. 39–44.
Website: http://sspnet.eu/2010/02/belfast-naturalistic/
说明: 数据集为视频，视频包括speech的情感识别
GEMEP Corpus
The GEneva Multimodal Emotion Portrayals (GEMEP) is a collection of audio and video recordings featuring 10 actors portraying 18 affective states, with different verbal contents and different modes of expression.
Modalities: Audio-visual
Reference: T. B¨anziger and K. Scherer, “Introducing the Geneva Multimodal Emotion Portrayal (GEMEP) Corpus,” in Blueprint for affective computing: A sourcebook, K. Scherer, T. B¨anziger, and E. Roesch, Eds. Oxford, England: Oxford University Press, 2010
Website: http://www.affective-sciences.org/gemep
　　　　 http://sspnet.eu/2011/05/gemep-fera/
说明: FERA2011比赛采用此数据集，标签主要是分类。
Paleari
Reference: M. Paleari, R. Chellali, and B. Huet, “Bimodal emotion recognition,” in Proceeding of the Second International Conference on Social Robotics ICSR’10, 2010, pp. 305–314.
该数据集我没找到它的官网，我查看了上面那个引用文章的摘要发现那篇文章不是介绍表情数据集的。那个文章在springer上，学校的网只能查到到摘要和第一章。
VAM corpus
The VAM corpus consists of 12 hours of recordings of the German TV talk-show “Vera am Mittag” (Vera at noon). They are segmented into broadcasts, dialogue acts and utterances, respectively. This audio -visual speech corpus contains spontaneous and very emotional speech recorded from unscripted, authentic discussions between the guests of the talk-show
Modalities: Audio-visual
Reference: M. Grimm, K. Kroschel, and S. Narayanan, “The Vera am Mittag German audio-visual emotional speech database,” in IEEE International Confernce on Multimedia and Expo ICME’08, 2008, pp. 865–868
Website: http://emotion-research.net/download/vam
说明: 该数据集主要是speech视频，标签为连续值，具体包括三个维度：valence (negative vs. positive), activation (calm vs. excited) and dominance (weak vs. strong)。
SSPNet Conflict Corpus（严格意义上不是表情识别数据集）
The “SSPNet Conflict Corpus” includes 1430 clips (30 seconds each) extracted from 45 political debates televised in Switzerland. The clips are in French
Modalities: Audio-visual
Reference: S.Kim, M.Filippone, F.Valente and A.Vinciarelli “Predicting the Conflict Level in Television Political Debates: an Approach Based on Crowdsourcing, Nonverbal Communication and Gaussian Processes“ Proceedings of ACM International Conference on Multimedia, pp. 793-796, 2012.
Website: http://www.dcs.gla.ac.uk/vincia/?p=270
说明: 该数据集主要是政治辩论中的视频，标签为conflict level。
Semaine database
The database contains approximately 240 character conversations, and recording is still ongoing. Currently approximately 80 conversations have been fully annotated for a number of dimensions in a fully continuous way using FeelTrace.
Website: http://semaine-db.eu/
Modalities: Audio-visual
Reference: The SEMAINE database: Annotated multimodal records of emotionally coloured conversations between a person and a limited agent G. Mckeown, M. F. Valstar, R. Cowie, M. Pantic, M. Schroeder. IEEE Transactions on Affective Computing. 3: pp. 5 - 17, Issue 1. April 2012.
说明: 通过人机对话来触发的视频，标签为连续的情感维度值，不是分类。
AFEW database(Acted Facial Expressions In The Wild)
Acted Facial Expressions In The Wild (AFEW) is a dynamic temporal facial expressions data corpus consisting of close to real world environment extracted from movies.
Reference: Abhinav Dhall, Roland Goecke, Simon Lucey, Tom Gedeon, Collecting Large, Richly Annotated Facial-Expression Databases from Movies, IEEE Multimedia 2012.
Website: https://cs.anu.edu.au/few/AFEW.html
Modalities: Audio-visual(电影剪辑片断)
说明: 该数据集的内容为从电影中剪辑的包含表情的视频片段，表情标签为六类基本表情+中性表情，annotation的信息保存在xml文件中。
AFEW数据集为Emotion Recognition In The Wild Challenge (EmotiW)系列情感识别挑战赛使用的数据集，该比赛从2013开始每年举办一次。
EmotiW官网：https://cs.anu.edu.au/few/
SFEW database(Static Facial Expressions in the Wild)
Static Facial Expressions in the Wild (SFEW) has been developed by selecting frames from AFEW
Reference: Abhinav Dhall, Roland Goecke, Simon Lucey, and Tom Gedeon. Static Facial Expressions in Tough Conditions: Data, Evaluation Protocol And Benchmark, First IEEE International Workshop on Benchmarking Facial Image Analysis Technologies BeFIT, IEEE International Conference on Computer Vision ICCV2011, Barcelona, Spain, 6-13 November 2011
Website: https://cs.anu.edu.au/few/AFEW.html
Modalities: Visual
说明: 该数据集是从AFEW数据集中抽取的有表情的静态帧，表情标签为六类基本表情+中性表情，annotation的信息保存在xml文件中。
AVEC系列数据集
AVEC是从2011开始每一年举办一次的表情识别挑战赛，表情识别的模型主要采用的连续情感模型。其中AVEC2012使用的情感维度为Arousal、Valence、Expectancy、Power； AVEC2013的情感维度为Valence和Arousal；AVEC2014的情感维度Valence、Arousal和Dominance。
AVEC2013和AVEC2014引入了depression recognition.
Modalities: Audio-visual
Website:
http://sspnet.eu/avec2011/
http://sspnet.eu/avec2012/
http://sspnet.eu/avec2013/
http://sspnet.eu/avec2014/
Reference: Michel Valstar , Björn W. Schuller , Jarek Krajewski , Roddy Cowie , Maja Pantic, AVEC 2014: the 4th international audio/visual emotion challenge and workshop, Proceedings of the ACM International Conference on Multimedia, November 03-07, 2014, Orlando, Florida, USA
说明:标签主要是针对的情感维度，通过csv的形式给出的。
LIRIS-ACCEDE数据集
LIRIS-ACCEDE数据集主要包含三个部分:
Discrete LIRIS-ACCEDE - Induced valence and arousal rankings for 9800 short video excerpts extracted from 160 movies. Estimated affective scores are also available.
Continuous LIRIS-ACCEDE - Continuous induced valence and arousal self-assessments for 30 movies. Post-processed GSR measurements are also available.
MediaEval 2015 affective impact of movies task - Violence annotations and affective classes for the 9800 excerpts of the discrete LIRIS-ACCEDE part, plus for additional 1100 excerpts used to extend the test set for the MediaEval 2015 affective impact of movies task.
Modalities: Audio-visual
Website:
http://liris-accede.ec-lyon.fr/index.php
Reference:
Y. Baveye, E. Dellandrea, C. Chamaret, and L. Chen, “LIRIS-ACCEDE: A Video Database for Affective Content Analysis,” in IEEE Transactions on Affective Computing, 2015.
Y. Baveye, E. Dellandrea, C. Chamaret, and L. Chen, “Deep Learning vs. Kernel Methods: Performance for Emotion Prediction in Videos,” in 2015 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII), 2015
M. Sjöberg, Y. Baveye, H. Wang, V. L. Quang, B. Ionescu, E. Dellandréa, M. Schedl, C.-H. Demarty, and L. Chen, “The mediaeval 2015 affective impact of movies task,” in MediaEval 2015 Workshop, 2015
说明: 该数据集既有离散的情感数据又有基于维度的情感数据。

几个重点参考的网站
http://emotion-research.net/wiki/Databases
http://sspnet.eu/category/sspnet_resource_categories/resource_type_classes/dataset/
http://ibug.doc.ic.ac.uk/resources
http://www.ecse.rpi.edu/~cvrl/database/other_facial_expression.htm

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