% *****************************General Info**************************************
The QMUL head dataset is formed by head images taken from the iLIDS dataset [1] portraying an airport indoor scenario. It is composed by 18667 images, uniformly partitioned into 5 classes: Back (BA), Front (FR), Left (LE), Right (RI), and Background (BG). Background images contain portions of the background scene. The images are 50x.50 pixels. Best performances here were achieved in [13]. The challenges of this dataset consist in scarce/non-homogeneous illumination, and quite severe occlusions. 

% *****************************Publications**************************************

[I] Diego Tosato, Michela Farenzena, Mauro Spera, Marco Cristani, Vittorio Murino.
Multi-class Classification on Riemannian Manifolds for Video Surveillance.
Proceedings of the IEEE ECCV 2010.  

% *****************************License Terms*************************************	
This dataset is made available to the scientific community for non-commercial 
research purposes such as academic research, teaching, scientific publications, 
or personal experimentation. Permission is granted to use, copy, and distribute
 the data given that you agree:

1) That the dataset comes "AS IS", without express or implied warranty. 
Although every effort has been made to ensure accuracy,  the University of Verona - VIPS lab, 
as website host) does not accept any responsibility for errors or omissions.
2) That you include a reference to the above publication in any published 
work that makes use of the dataset.
3) That if you have altered the content of the dataset or created derivative work, 
prominent notices are made so that any recipients know that they are not receiving 
the original data.
4) That you may not use or distribute the dataset or any derivative work for
 commercial purposes as, for example, licensing or selling the data, or using
 the data with a purpose to procure a commercial gain.
5)That this original license notice is retained with all copies or derivatives 
of the dataset.
That all rights not expressly granted to you are reserved by 
University of Verona - VIPS lab.


% *****************************References**************************************

[1] "i-LIDS",http://tna.europarchive.org/20100413151426/scienceandresearch.homeoffice.gov.uk/hosdb/cctv-imaging-technology/i-lids/index.html
[2] J. Orozco, S. Gong, and T. Xiang, “Head pose classification in Crowded Scenes,” in Proc. BMVC, 2009.