paper 148: Face Databases

Database	Year of release	# subjects	# videos	Acquisition camera device	Attack type	Subject race	Subject gender	Subject age
NUAA [1]	2010	15	•24 genuine • 33 spoof	• Web-cam (640 × 480)	• Printed photo	• Asian 100%	• Male 80% • Female 20%	20 to 30 yrs
Idiap REPLAYATTACK [2][3][4]	2012	50	• 200 genuine • 1,000 spoof	• MacBook 13’’ camera (320 × 240)	• Printed photo • Display photo (mobile/HD) • Replayed video (mobile/HD)	• White 76% • Asian 22% • Black 2%	• Male 86% • Female 14%	20 to 40 yrs
CASIA FASD [5]	2012	50	• 150 genuine • 450 spoof	• Low-quality camera (640 × 480) • Normal-quality camera (480 × 640) • Sony NEX-5 camera (1280 × 720)	• Printed photo •Cut photo^② • Replayed video (HD)	• Asian 100%	• Male 86% • Female 14%	20 to 35 yrs
MSU MFSD [6]	2014	55^①	• 110 genuine • 330 spoof	• MacBook Air 13” camera (640 × 480) • Google Nexus 5 camera (720 × 480)	• Printed photo • Replayed video (mobile/HD)	• White 70% • Asian 28% • Black 2%	• Male 63% • Female 37%	20 to 60 yrs
The Oulu-NPU face anti-spoofing database	2016	55	• 990 genuine • 3,960 spoof	• Front cameras of six mobile devices（1080×1920） (Samsung Galaxy S6 edge, HTC Desire EYE, MEIZU X5, ASUS Zenfone Selfie, Sony XPERIA C5 Ultra Dual and OPPO N3)	• Two printed photo • Two replayed video (mobile/HD)	• White 4% • Asian 96%	• Male 69% • Female31%	20 to 60 yrs

注：① MSU MFSD中55个人的数据，只有35个人的数据可以公开使用。

②“Cut photo attack”表示将打印图片中眼睛部位剪掉，攻击者用他的照片盖住他的脸，并且可以在洞里眨眼睛。

6、USTC-NVIE Database[(natural visible and infrared facial expression database)]

该数据库是由中国科学技术大学安徽省计算与通信软件重点实验室建成并发布，是目前世界较为全面的人脸表情数据库，其中包含大约100名被试三种光照条件下六种表情的可见图像以及长波红外图像，另外表情又分为自发表情与人为表情，人为表情又分为戴眼镜与不戴眼镜两种情况。为进行（自发+人为）表情识别与情绪分析推理实验提供了充足的实验样本与数据。

数据库主页：http://nvie.ustc.edu.cn/

发布地址：http://sspnet.eu/2010/08/ustc-nvie-natural-visible-and-infrared-facial-expression-database/

■Annotated Database (Hand, Meat, LV Cardiac, IMM face) (Link)

■AR Face Database (Link)

■BioID Face Database (Link)

■Caltech Computational Vision Group Archive (Cars, Motorcycles, Airplanes, Faces, Leaves, Background) (Link)

■Carnegie Mellon Image Database (motion, stereo, face, car, ...) (Link)

■CAS-PEAL Face Database (Link)

■CMU Cohn-Kanade AU-Coded Facial Expression Database (Link)

■CMU Face Detection Databases (Link)

■CMU Face Expression Database (Link)

■CMU Face Pose, Illumination, and Expression (PIE) Database (Link)

■CMU VASC Image Database (motion, road sequences, stereo, CIL’s stereo data with ground truth, JISCT, face, face expressions, car) (Link)

■Content-based Image Retrieval Database (Link)

■Face Video Database of the Max Planck Institute for Biological Cybernetics (Link)

■FERET Database (Link)

■FERET Color Database (Link)

■Georgia Tech Face Database (Link)

■German Fingerspelling Database (Link)

■Indian Face Database ([url=http://vis-www.cs.umass.edu/~vidit/IndianFaceDatabase/]Link[/url])

■MIT-CBCL Car Database (Link)

■MIT-CBCL Face Recognition Database (Link)

■MIT-CBCL Face Databases (Link)

■MIT-CBCL Pedestrian Database (Link)

■MIT-CBCL Street Scenes Database (Link)

■NIST/Equinox Visible and Infrared Face Image Database (Link)

■NIST Fingerprint Data at Columbia (Link)

■ORL Database of Faces (Link)

■Rutgers Skin Texture Database (Link)

■The Japanese Female Facial Expression (JAFFE) Database (Link)

■The Ohio State University SAMPL Image Database (3D, still, motion) (Link)

■The University of Oulu Physics-Based Face Database (Link)

■UMIST Face Database (Link)

■USF Range Image Data (with ground truth) (Link)

■Usenix Face Database (hundreds of images, several formats) (Link)

■UCI Machine Learning Repository (Link)

■USC-SIPI Image Database (collection of digitized images) (Link)

■UCD VALID Database (multimodal for still face, audio, and video) (Link)

■UCD Color Face Image (UCFI) Database for Face Detection (Link)

■UCL M2VTS Multimodal Face Database (Link)

■Vision Image Archive at UMass (sequences, stereo, medical, indoor, outlook, road, underwater, aerial, satellite, space and more) (Link)

■Where can I find Lenna and other images? (Link)

■Yale Face Database (Link)

■Yale Face Database B (Link)

7、Other Database

1、Humaine - a collection of emotional databases:
http://emotion-research.net/wiki/Databases

2、AR Face Database (AR):
http://rvl1.ecn.purdue.edu/~aleix/aleix_face_DB.html
3、BioID Face Database (BioID):
http://www.humanscan.de/support/downloads/facedb.php
4、Brodatz Texture Database (Brodatz):
5、Butterfly Database (BDB):
http://www-cvr.ai.uiuc.edu/ponce_grp/data
6、CMU Frontal Face Database (CMUFF):
http://vasc.ri.cmu.edu//idb/html/face/frontal_images/index.html
7、CMU PIE Database (CMUPIE):
http://www.ri.cmu.edu/projects/project_418.html
8、CMU Profile Face Database (CMUPF):
http://vasc.ri.cmu.edu//idb/html/face/profile_images/index.html
9、Columbia-Utrecht Reflectance and Texture Database (CUReT):
10、Corel Gallery Magic 65000 (CGM):
11、CVL Database (CVL):
http://www.lrv.fri.uni-lj.si/facedb.html
12、Data Becker 222222 Premium Cliparts (DBPC):
13、M2VTS Multimodal Face Database ():
http://www.tele.ucl.ac.be/PROJECTS/M2VTS/m2fdb.html
14、MIT CBCL Car Database (MITC):
http://cbcl.mit.edu/cbcl/software-datasets/CarData.html
15、MIT CBCL Face Database (MITF):
http://cbcl.mit.edu/cbcl/software-datasets/FaceData2.html

16、MIT CBCL Face Recognition Database ():
http://cbcl.mit.edu/software-datasets/heisele/facerecognition-database.html
17、MIT CBCL Pedestrian Database (MITP):
http://cbcl.mit.edu/cbcl/software-datasets/PedestrianData.html
18、Object Recognition Database (ORDB):
http://www-cvr.ai.uiuc.edu/ponce_grp/data
19、ORL Database of Faces (ORL):
http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html
20、OUTex (OUTex):
21、PETS 2000 Dataset (PETS2000):
22、PETS 2001 Dataset (PETS2001):
23、PETS 2002 Dataset (PETS2002):
24、PETS 2005 Dataset (PETS2005):
25、PETS-ECCV 2004 Dataset (PETSECCV2004):
26、PETS-ICVS 2003 Dataset (PETSICVS2003):
27、PhoTex (PhoTex):
28、Pilot European Image Processing Archive (PEIPA):
http://peipa.essex.ac.uk/
29、Talking Face Video ():
30、Texture Database (TDB):
http://www-cvr.ai.uiuc.edu/ponce_grp/data
31、Texture Database for the Measurement of Texture classification algorithms (MeasTex):
32、The Color FERET Database ():
http://www.itl.nist.gov/iad/humanid/colorferet/home.html
33、The Extended M2VTS Database (XM2VTSDB ):
http://www.ee.surrey.ac.uk/Research/VSSP/xm2vtsdb/
34、The FERET Database ():
http://www.itl.nist.gov/iad/humanid/feret/
35、The Japanese Female Facial Expression (JAFFE) Database (JAFFE):
http://www.kasrl.org/jaffe.html
36、The M2VTS Database (M2VTS): http://www.tele.ucl.ac.be/PROJECTS/M2VTS/m2fdb.html
37、The Psychological Image Collection at Stirling (PICS):
http://pics.psych.stir.ac.uk/cgi-bin/PICS/New/pics.cgi
38、The UMIST Face Database (UMIST):
39、The University of Oulu Physics-Based Face Database (UOFD):
http://www.ee.oulu.fi/research/imag/color/pbfd.html
40、The Yale Face Database (YFD):
http://cvc.yale.edu/projects/yalefaces/yalefaces.html
41The Yale Face Database B (YFDB):
http://cvc.yale.edu/projects/yalefacesB/yalefacesB.html

42、Vision Texture Database (VisTex):
43、VS-PETS 2003 Dataset (VSPETS2003):

Other useful links:（上述数据库及其它数据库的简介）
1、http://www.face-rec.org/databases/
2、http://web.mit.edu/emeyers/www/face_databases.html

参考来源：[1] 严严,陈日伟,王菡子.基于深度学习的人脸分析研究进展[J].厦门大学学报(自然科学版),2017,56(1):13-24.

[2] Di Wen, Member, IEEE, Hu Han, Member, IEEE and Anil K. Jain, “Face Spoof Detection with Image Distortion Analysis”, in IEEE Transactions on Information Forensics and

Security,2015,pp.1–16.

[3] http://blog.csdn.net/u012374174/article/details/71420766

[4] http://www.cnblogs.com/leivo/archive/2008/11/11/1331675.html