TY - GEN
T1 - The challenge of face recognition from digital point-and-shoot cameras
AU - Beveridge, J. Ross
AU - Phillips, P. Jonathon
AU - Bolme, David S.
AU - Draper, Bruce A.
AU - Given, Geof H.
AU - Lui, Yui Man
AU - Teli, Mohammad Nayeem
AU - Zhang, Hao
AU - Scruggs, W. Todd
AU - Bowyer, Kevin W.
AU - Flynn, Patrick J.
AU - Cheng, Su
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84893769964&partnerID=8YFLogxK
U2 - 10.1109/BTAS.2013.6712704
DO - 10.1109/BTAS.2013.6712704
M3 - Conference contribution
AN - SCOPUS:84893769964
SN - 9781479905270
T3 - IEEE 6th International Conference on Biometrics: Theory, Applications and Systems, BTAS 2013
BT - IEEE 6th International Conference on Biometrics
PB - IEEE Computer Society
T2 - 6th IEEE International Conference on Biometrics: Theory, Applications and Systems, BTAS 2013
Y2 - 29 September 2013 through 2 October 2013
ER -