QMUL (Queen Mary University of London) Head Pose Dataset




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.

Data

Original Orozco's database download (it contains the descriptors proposed in [2]): [QMULPoseHeadsOROZCO
Dataset download

Publications

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

D. Tosato, M. Spera, M. Cristani, and V. Murino, Characterizing humans on
Riemannian manifolds, IEEE Transactions on Pattern Analysis and Machine
Intelligence (TPAMI), Accepted 2012. [pdf]


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.