资源论文The MegaFace Benchmark: 1 Million Faces for Recognition at Scale

The MegaFace Benchmark: 1 Million Faces for Recognition at Scale

2019-12-23 | |  70 |   64 |   0

Abstract

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 fifirst 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 identififi- cation and verifification 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

上一篇:Augmented Blendshapes for Real-time Simultaneous 3D Head Modeling and Facial Motion Capture

下一篇:Mirror Surface Reconstruction under an Uncalibrated Camera

用户评价
全部评价

热门资源

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Hierarchical Task...

    We extend hierarchical task network planning wi...

  • Shape-based Autom...

    We present an algorithm for automatic detection...