Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization*

Martin Koestinger, Paul Wohlhart, Peter M. Roth, Horst Bischof
Institute for Computer Graphics and Vision, Graz University of Technology

*The work was supported by the FFG projects MDL (818800) and SECRET (821690) under the Austrian Security Research Programme KIRAS.


This page briefly describes our AFLW databse ...

It provides:


2012-11-28 New database release! This release includes revised and corrected annotations for roughly 2k faces and also new import scripts for various other face databases (PUT,LFPW,BioId,FaceTracer). See the changelog in the archive for details.

2012-01-10 Updated sqlite3 database file and tools, see the changelog in the archive for details.

2011-12-23 First release of AFLW


Annotated Facial Landmarks in the Wild (AFLW) provides a large-scale collection of annotated face images gathered from Flickr, exhibiting a large variety in appearance (e.g., pose, expression, ethnicity, age, gender) as well as general imaging and environmental conditions. In total about 25k faces are annotated with up to 21 landmarks per image. A short comparison to other important face databases with annotated landmarks is provided here:


# landmarked imgs.# landmarks# subjects image size image color Ref.

Caltech 10,000 Web Faces

10,524 4 - - color [1]

CMU / VASC Frontal

734 6 - - grayscale [10]

CMU / VASC Profile

590 6 to 9 - - grayscale [11]


240 58 40 648x480 color/grayscale[9]


401 80 26 896x896 color [8]

AR Purdue

508 22 116 768x576 color [5]


1,521 20 23 384x286 grayscale [3]


2,360 68 295 720x576 color [6]


2,880 52 4 640480 color [2]


3,755 76 276 480x640 color [7]


9,971 30 100 2048x1536 color [4]


25,993 21 - - color

Table 1: Face databases with annotated facial landmarks.


The motivation for the AFLW database is the need for a large-scale, multi-view, real-world face database with annotated facial features. We gathered the images on Flickr using a wide range of face relevant tags (e.g., face, mugshot, profile face). The downloaded set of images was manually scanned for images containing faces. The key data and most important properties of the database are:

Due to the nature of the database and the comprehensive annotation we think it is well suited to train and test algorithms for

License agreement

By downloading the database you agree to the following restrictions:

  1. The AFLW database is available for non-commercial research purposes only.
  2. The AFLW database includes images obtained from FlickR which are not property of Graz University of Technology. Graz University of Technology is not responsible for the content nor the meaning of these images. Any use of the images must be negociated with the respective picture owners, according to the Yahoo terms of use. In particular, you agree not to reproduce, duplicate, copy, sell, trade, resell or exploit for any commercial purposes, any portion of the images and any portion of derived data.
  3. You agree not to further copy, publish or distribute any portion of the AFLW database. Except, for internal use at a single site within the same organization it is allowed to make copies of the database.
  4. All submitted papers or any publicly available text using the AFLW database must cite the following paper:

    Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial Landmark Localization (pdf, bibtex)
    Martin Koestinger, Paul Wohlhart, Peter M. Roth, and Horst Bischof
    In First IEEE International Workshop on Benchmarking Facial Image Analysis Technologies, 2011

  5. The organization represented by you will be listed as users of the AFLW database.

Download instructions

If you agree with the terms of the license agreement contact Michael Opitz (michael.opitz(at) to obtain download instructions.
Please send the email from your official account so we can verify your affiliation and include your

If you already received your login credentials you can proceed to the download section.

Additional notes


The work was supported by the FFG projects MDL (818800) and SECRET (821690) under the Austrian Security Research Programme KIRAS. We want to thank all people who have been involved in the annotation process, especially, the interns at the institute and the colleagues from the Documentation Center of the National Defense Academy of Austria.


[1]   A. Angelova, Y. Abu-Mostafam, and P. Perona. Pruning training sets for learning of object categories. In Proc. CVPR, 2005.

[2]    O. Aran, I. Ari, M. A. Guvensan, H. Haberdar, Z. Kurt, H. I. Turkmen, A. Uyar, and L. Akarun. A database of non-manual signs in turkish sign language. In Proc. Signal Processing and Communications Applications, 2007.

[3]   O. Jesorsky, K. J. Kirchberg, and R. W. Frischholz. Robust face detection using the Hausdorff distance. In Proc. Audio and Video-based Biometric Person Authentication, 2001.

[4]   S. A. Kasiński A., Florek A. The PUT face database. Image Processing & Communications, pages 59–64, 2008.

[5]   A. Martinez and R. Benavente. The AR face database. Technical Report 24, Computer Vision Center, University of Barcelona, 1998.

[6]   K. Messer, J. Matas, J. Kittler, and K. Jonsson. XM2VTSDB: The extended M2VTS database. In Proc. Audio and Video-based Biometric Person Authentication, 1999.

[7]   S. Milborrow, J. Morkel, and F. Nicolls. The MUCT Landmarked Face Database. In Proc. Pattern Recognition Association of South Africa, 2010.

[8]   C. P. N. Aifanti and A. Delopoulos. The MUG facial expression database. In Proc. Workshop on Image Analysis for Multimedia Interactive Services, 2005.

[9]   M. M. Nordstrom, M. Larsen, J. Sierakowski, and M. B. Stegmann. The IMM face database - an annotated dataset of 240 face images. Technical report, Informatics and Mathematical Modelling, Technical University of Denmark, DTU, 2004.

[10]   H. Rowley, S. Baluja, and T. Kanade. Rotation invariant neural network-based face detection. Technical Report CMU-CS-97-201, Computer Science Department, Carnegie Mellon University (CMU), 1997.

[11]   H. Schneiderman and T. Kanade. A statistical model for 3D object detection applied to faces and cars. In Proc. CVPR, 2000.

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