微表情数据集汇总(全)

最近一段时间收集了一些微表情的数据集，主要有两个目的，一是做一个汇总，二是为了正在找相关数据集的同学提供一个方便。如果你有文中未提到的数据集可以在下面留言附上链接，万分感谢~

CK+:http://www.pitt.edu/~emotion/ck-spread.htm
MMI:https://mmifacedb.eu/
FER-2013:https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge
AFEW 7.0:https://sites.google.com/site/emotiwchallenge/
SFEW 2.0: https://cs.anu.edu.au/few/emotiw2015.html
Multi-PIE: http://www.flintbox.com/public/project/4742/
BU-3DFE: http://www.cs.binghamton.edu/~lijun/Research/3DFE/3DFE_Analysis.html
Oulu-CASIA: http://www.cse.oulu.fi/CMV/Downloads/Oulu-CASIA
RaFD: http://www.socsci.ru.nl:8180/RaFD2/RaFD
KDEF: http://www.emotionlab.se/kdef/
EmotioNet: http://cbcsl.ece.ohio-state.edu/dbform_emotionet.html
RAF-DB: http://www.whdeng.cn/RAF/model1.html
AffectNet: http://mohammadmahoor.com/databases-codes/
ExpW: http://mmlab.ie.cuhk.edu.hk/projects/socialrelation/index.html

Image Database of Facial Actions and Expressions - Expression Image Database
24 subjects are represented in this database, yielding between about 6 to 18 examples of the 150 different requested actions. Thus, about 7,000 color images are included in the database, and each has a matching gray scale image used in the neural network analysis.

Japanese Female Facial Expression (JAFFE) Database
The database contains 213 images of 7 facial expressions (6 basic facial expressions + 1 neutral) posed by 10 Japanese female models. Each image has been rated on 6 emotion adjectives by 60 Japanese subjects.

Caltech Faces
450 face images. 896 x 592 pixels. JPEG format. 27 or so unique people under with different lighting/expressions/backgrounds.

Indian Face Database
The database contains a set of face images taken in February, 2002 in the IIT Kanpur campus. There are eleven different images of each of 40 distinct subjects. For some subjects, some additional photographs are included. All the images were taken against a bright homogeneous background with the subjects in an upright, frontal position. The files are in JPEG format. The size of each image is 640x480 pixels, with 256 grey levels per pixel. The images are organized in two main directories - males and females. In each of these directories, there are directories with name as a serial numbers, each corresponding to a single individual. In each of these directories, there are eleven different images of that subject, which have names of the form abc.jpg, where abc is the image number for that subject. The following orientations of the face are included: looking front, looking left, looking right, looking up, looking up towards left, looking up towards right, looking down. Available emotions are: neutral, smile, laughter, sad/disgust.

The Bosphorus Database
The Bosphorus Database is a new 3D face database that includes a rich set of expressions, systematic variation of poses and different types of occlusions. This database is unique from three aspects: (1) The facial expressions are composed of judiciously selected subset of Action Units as well as the six basic emotions, and many actors/actresses are incorporated to obtain more realistic expression data; (2) A rich set of head pose variations are available; (3) Different types of face occlusions are included. Hence, this new database can be a very valuable resource for development and evaluation of algorithms on face recognition under adverse conditions and facial expression analysis as well as for facial expression synthesis.

McGill Real-world Face Video Database
This database contains 18000 video frames of 640x480 resolution from 60 video sequences, each of which recorded from a different subject (31 female and 29 male). Each video was collected in a different environment (indoor or outdoor) resulting arbitrary illumination conditions and background clutter. Furthermore, the subjects were completely free in their movements, leading to arbitrary face scales, arbitrary facial expressions, head pose (in yaw, pitch and roll), motion blur, and local or global occlusions.

Indian Movie Face database (IMFDB)
Indian Movie Face database (IMFDB) is a large unconstrained face database consisting of 34512 images of 100 Indian actors collected from more than 100 videos. All the images are manually selected and cropped from the video frames resulting in a high degree of variability interms of scale, pose, expression, illumination, age, resolution, occlusion, and makeup. IMFDB is the first face database that provides a detailed annotation of every image in terms of age, pose, gender, expression and type of occlusion that may help other face related applications.

Senthil IRTT Face Database Version 1.1
This database contains IRTT (Institute of Road and Transport Technology) students of both colour and gray scale facial images. There are 317 facial images for 13 IRTT students. They are of same age factor around 23 to 24 years. The images along with background are captured by canon digital camera of 14.1 megapixels resolution. The actual size of cropped faces 550x780 and they are further resized to downscale factor 5. Out of 13, 12 male and one female. Each subject have variety of face expressions, little makeup, scarf, poses and hat also.

VT-AAST Bench-marking Dataset
Virginia Tech - Arab Academy for Science & Technology (VT-AAST) Bench-marking Dataset is a color face image database for benchmarking of automatic face detection algorithms and human skin segmentation techniques. It is named the VT-AAST image database, and is divided into four parts. Part one is a set of 286 color photographs that include a total of 1027 faces in the original format given by our digital cameras, offering a wide range of difference in orientation, pose, environment, illumination, facial expression and race. Part two contains the same set in a different file format. The third part is a set of corresponding image files that contain human colored skin regions resulting from a manual segmentation procedure. The fourth part of the database has the same regions converted into grayscale. The database is available on-line for noncommercial use.

Facial Expression Research Group Database (FERG-DB)
Facial Expression Research Group Database (FERG-DB) is a database of stylized characters with annotated facial expressions. The database contains multiple face images of six stylized characters. The characters were modelled using the MAYA software and rendered out in 2D to create the images. The database contains facial expression images of six stylized characters. The images for each character is grouped into seven types of expressions - anger, disgust, fear, joy, neutral, sadness and surprise.

PIE Database, CMU
A database of 41,368 images of 68 people, each person under 13 different poses, 43 different illumination conditions, and with 4 different expressions.

AT&T "The Database of Faces" (formerly "The ORL Database of Faces")
Ten different images of each of 40 distinct subjects. For some subjects, the images were taken at different times, varying the lighting, facial expressions (open / closed eyes, smiling / not smiling) and facial details (glasses / no glasses). All the images were taken against a dark homogeneous background with the subjects in an upright, frontal position (with tolerance for some side movement).

Georgia Tech Face Database
The database contains images of 50 people and is stored in JPEG format. For each individual, there are 15 color images captured between 06/01/99 and 11/15/99. Most of the images were taken in two different sessions to take into account the variations in illumination conditions, facial expression, and appearance. In addition to this, the faces were captured at different scales and orientations.

NLPR Face Database
450 face images. 896 x 592 pixels. JPEG format. 27 or so unique people under with different lighting/expressions/backgrounds.

The AR Face Database, The Ohio State University, USA
4,000 color images corresponding to 126 people's faces (70 men and 56 women). Images feature frontal view faces with different facial expressions, illumination conditions, and occlusions (sun glasses and scarf).

Specs on Faces (SoF) Dataset
The SoF dataset is a collection of 42,592 (2,662×16) images for 112 persons (66 males and 46 females) who wear glasses under different illumination conditions. The dataset is FREE for reasonable academic fair use. The dataset presents a new challenge regarding face detection and recognition. It is devoted to two problems that affect face detection, recognition, and classification, which are harsh illumination environments and face occlusions. The glasses are the common natural occlusion in all images of the dataset. However, the glasses are not the sole facial occlusion in the dataset; there are two synthetic occlusions (nose and mouth) added to each image. Moreover, three image filters, that may evade face detectors and facial recognition systems, were applied to each image. All generated images are categorized into three levels of difficulty (easy, medium, and hard). That enlarges the number of images to be 42,592 images (26,112 male images and 16,480 female images). Furthermore, the dataset comes with a metadata that describes each subject from different aspects. The original images (without filters or synthetic occlusions) were captured in different countries over a long period. // Usage: 1 - Gender classification; 2 - Face detection; 3 - Facial landmark estimation; 4 - Emotion Recognition; 5 - Eyeglasses detection; 6 - Age classification.

Disguised Faces in the Wild （这个我没下成功，下好的同学可以留言个百度盘）
Face recognition research community has prepared several large-scale datasets captured in uncontrolled scenarios for performing face recognition. However, none of these focus on the specific challenge of face recognition under the disguise covariate. The Disguised Faces in the Wild (DFW) dataset has been prepared in order to address these limitations. The proposed DFW dataset consists of 11,157 images of 1,000 subjects. The dataset contains a broad set of unconstrained disguised faces, taken from the Internet. The dataset encompasses several disguise variations with respect to hairstyles, beard, mustache, glasses, make-up, caps, hats, turbans, veils, masquerades and ball masks. This is coupled with other variations with respect to pose, lighting, expression, background, ethnicity, age, gender, clothing, hairstyles, and camera quality, thereby making the dataset challenging for the task of face recognition. The paper describing the database and the protocols is available here.

 VIP_attribute Dataset
Images in the VIP_attribute dataset are obtained in 2017 from the WWW corresponding to 513 female and 513 male subjects (mainly actors, singers and athletes). The images include the frontal pose of the subjects. Co-variates include illumination, expression, image quality and resolution. Further challenging in this dataset are beautification (e.g., photoshop) of the images, as well as the presence of makeup, plastic surgery, beard and mustache. We obtained annotations related to te subjects' body weight and height from websites such as www.celebheights.com, www.howtallis.org and celebsize.com.

##Reference
http://www.face-rec.org/databases/
https://blog.csdn.net/mathlxj/article/details/87920084
https://www.cnblogs.com/lile-tju/p/8058969.html