The challenge of face recognition from digital point-and-shoot cameras

J. Ross Beveridge, P. Jonathon Phillips, David S. Bolme, Bruce A. Draper, Geof H. Givens, Yui Man Lui, Mohammad Nayeem Teli, Hao Zhang, W. Todd Scruggs, Kevin W. Bowyer, Patrick J. Flynn, Su Cheng

Abstract: Inexpensive “point-and-shoot” camera technology has combined with social network technology to give the general population a motivation 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 this paper introduces the Point-and-Shoot Face Recognition Challenge (PaSC). The challenge includes 9,376 still images of 293 people balanced with respect to distance to the camera, alternative sensors, frontal versus not-frontal views, and varying location. There are also 2,802 videos for 265 people: a subset of the 293. 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.