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

Inexpensive “point-and-shoot ” camera technology has combined with social network technology to give the gen-eral population a motivation to use face recognition tech-nology. Users expect a lot; they want to snap pictures, shoot videos, upload, and have their friends, family and acquain-tances 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 pa-per introduces the Point-and-Shoot Face Recognition Chal-lenge (PaSC). The challenge includes 9,376 still images of 293 people balanced with respect to distance to the cam-era, 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 pre-sented for public baseline algorithms and a commercial al-gorithm for three cases: comparing still images to still im-ages, videos to videos, and still images to videos. 1