a) FaceScrub + MegaFace (b) FGNET + MegaFace Figure 1 . The MegaFace challenge evaluates identification and verification as a function of increasing number of gallery distractors (going from 10 to 1 Million). We use two different probe sets (a) FaceScrub-photos of celebrities, (b) FGNET-photos with a large variation in age per person. We present rank-1 identification of state of the art algorithms that participated in our challenge. On the left side of each plot is current major benchmark LFW scale (i.e., 10 distractors, see how all the top algorithms are clustered above 95%). On the right is mega-scale (with a million distractors). Observe that rates drop with increasing numbers of distractors, even though the probe set is fixed, and that algorithms trained on larger sets (dashed lines) generally perform better. Participate at: http://megaface.cs.washington.edu. Recent face recognition experiments on a major benchmark (LFW [15]) show stunning performance-a number of algorithms achieve near to perfect score, surpassing human recognition rates. In this paper, we advocate evaluations at the million scale (LFW includes only 13K photos of 5K people). To this end, we have assembled the MegaFace dataset and created the first MegaFace challenge. Our dataset includes One Million photos that capture more than 690K different individuals. The challenge evaluates performance of algorithms with increasing numbers of "distractors" (going from 10 to 1M) in the gallery set. We present both identification and verification performance, evaluate performance with respect to pose and a persons age, and compare as a function of training data size (#photos and #people). We report results of state of the art and baseline algorithms. The MegaFace dataset, baseline code, and evaluation scripts, are all publicly released for further experimentations 1 . 1 MegaFace data, code, and challenge can be found at:
Motivated by the performance saturation on some major benchmarks (i.e., LFW), where a number of algorithms achieved near to perfect score, surpassing human recognition rates, Kemelmacher-Shlizerman et al. REF proposed evaluations at the million scale by assembling the MegaFace dataset.
The MegaFace Benchmark: 1 Million Faces for Recognition at Scale
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Computer Science