Face recognition is appearing in personal and commercial products at an astonishing rate, yet reliable face recognition remains challenging. Users expect a lot; they want to snap pictures and have their friends, family and acquaintances recognized. This scenario is playing out millions of times a day, and despite its simplicity face recognition in this context is hard. Roughly speaking, failure rates in the 4 to 8 out of 10 range are common. In contrast, using well controlled imagery error rates drop as low as 1 in 1,000. To spur advancement on point-and-shoot face recognition this paper presents a new challenge problem consisting of 9,376 images of 293 people balanced with respect to distance to the camera, alternative sensors, frontal versus not-frontal views, and varying location. Results are presented for two public baseline algorithms and a high-quality commercial algorithm. At false accept rates of 0.001 and 0.01 the best verification rates presented are 0.21 and 0.41 respectively. Pilot studies illustrate covariates analysis, non-match score sensitivity to covariates and an image quality analysis suggesting commonly suggested quality measures dont capture what is making the problem challenging.
Proceedings Title: IEEE Conference on Computer Vision and Pattern Recognition
Conference Dates: June 25-27, 2013
Conference Location: Portland, OR
Pub Type: Conferences