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Point and Shoot Face Recognition Challenge (PaSC)

Inexpensive "point-and-shoot" camera technology combined with social network technology motivates the general population to use face recognition technology. Users expect a lot; they want to snap pictures, shoot videos, upload, and have their friends, family and acquaintances more-or-less automatically recognized. Despite the apparent simplicity of the problem, face recognition in this context is hard. Roughly speaking, failure rates in the 4 to 8 out of 10 range are common. In contrast, error rates drop to roughly 1 in 1,000 for well-controlled imagery. To spur advancement in face and person recognition we created the Point and Shoot Face Recognition Challenge (PaSC).  

The challenge includes 9,376 still images and 2,802 videos of 293 people. The images are balanced with respect to distance to the camera, alternative sensors, frontal versus not-frontal views, and different locations. Verification results are presented for public baseline algorithms and a commercial algorithm for three cases: comparing still images to still images, videos to videos, and still images to videos.  

Details of the PaSC can be found in the paper "The Challenge of Face Recognition from Digital Point-and-Shoot Cameras,'' J. R. Beveridge, P. J. Phillips, D. Bolme, B. A. Draper, G. H. Givens, Y-M. Lui, M. N. Teli, H. Zhang, W. T. Scruggs, K. W. Bowyer, P. J. Flynn, S. Cheng. Details on the implementation of the PaSC can be found at http://www.cs.colostate.edu/pasc/.  

The PaSC is a collaborative effort between NIST and the Colorado State University. The data supporting the PaSC was collected at the University of Notre Dame.

In order to get the PaSC distribution, researchers will need to:

  1. Obtain the PaSC data set by following the instructions for the PaSC license on http://www3.nd.edu/~cvrl/CVRL/Data_Sets.html
  2. Download the PaSC data set
  3. Download the PaSC specifications, infrastructure, and baseline algorithms from http://www.cs.colostate.edu/pasc/.  
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