The MegaFace Benchmark: 1 Million Faces for Recognition at Scale

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 experimentations1.

1. Introduction

Face recognition has seen major breakthroughs in the last couple of years, with new results by multiple groups [25], [29], [27] surpassing human performance on the leading Labeled Faces in the Wild (LFW) benchmark [15] and achieving near perfect results¹

MegaFace data, code, and challenge can be found at: http://megaface.cs.washington.edu

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