3D Keypoints
2D Box
2D Ellipse
许可协议: Custom


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:

  • The database contains about 25k annotated faces in real-world images. Of these faces 59% are tagged as female, 41% are tagged as male (updated); some images contain multiple faces. No rescaling or cropping has been performed. Most of the images are color although some of them gray-scale.
  • In total AFLW contains roughly 380k manually annotated facial landmarks of a 21 point markup. The facial landmarks are annotated upon visibility. So no annotation is present if a facial landmark, e.g., left ear lobe, is not visible.
  • A wide range of natural face poses is captured The database is not limited to frontal or near frontal faces.
  • Additional to the landmark annotation the database provides face rectangles and ellipses. The ellipses are compatible with the FDDB protocol. Further, we include the coarse head pose obtained by fitting a mean 3D face with the POSIT algorithm.
  • A rich set of tools to work with the annotations is provided, e.g., a database backend that enables to import other face collections and annotation types. Also a graphical user interface is provided that enables to view and manipulate the annotations.

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

  • facial feature localization
  • multi-view face detection
  • coarse head pose estimation.

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:

Database # landmarked imgs # landmarks # subjects image size image color Ref.
Caltech 10,000 Web Faces 10,524 - - - color [1]
CMU/VASC Frontal 734 6 - - grayscale [10]
CMU/VASC Profile 590 6 to 9 - - grayscale [11]
IMM 240 58 40 640x480 color/grayscale [9]
MUG 401 80 26 896x896 color [8]
AR Purdue 508 22 116 768x576 color [5]
BioID 1,521 20 23 384x286 grayscale [3]
XM2VTS 2,360 68 295 720x576 color [6]
BUHMAP-DB 2,880 52 4 640x480 color [2]
MUCT 3,755 76 276 480x640 color [7]
PUT 9,971 30 100 2048x1536 color [4]
AFLW 25,993 21 - - color


Please use the following citation when referencing the dataset:

 author = {Martin Koestinger, Paul Wohlhart, Peter M. Roth and Horst Bischof},
 title = {{Annotated Facial Landmarks in the Wild: A Large-scale, Real-world Database for Facial
Landmark Localization}},
 booktitle = {{Proc. First IEEE International Workshop on Benchmarking
Facial Image Analysis Technologies}},
 year = {2011}



ICG(Institute of Computer Graphics and Vision)
We are the only Austrian academic group with the charter to address all of visual computing, encompassing both computer vision and computer graphics. Our research is focused on visualization, rendering, virtual reality, augmented reality, object recognition and reconstruction, machine learning, medical imaging and robot vision.