Face re-identification challenge: Are face recognition models good enough?
作者：
Highlights：
• We construct the largest and only face re-identification benchmark with native surveillance facial imagery data, the Surveillance Face Re-ID Challenge (SurvFace).
• We benchmark representative deep learning face-recognition models on the SurvFace challenge, in a more realistic open-set scenario, originally missing in the previous studies.
• We investigate extensively the performance of existing models on SurvFace by exploiting simultaneously image super-resolution and face-recognition models.
• We provide extensive discussions on future research directions for face re-identification.
摘要
•We construct the largest and only face re-identification benchmark with native surveillance facial imagery data, the Surveillance Face Re-ID Challenge (SurvFace).•We benchmark representative deep learning face-recognition models on the SurvFace challenge, in a more realistic open-set scenario, originally missing in the previous studies.•We investigate extensively the performance of existing models on SurvFace by exploiting simultaneously image super-resolution and face-recognition models.•We provide extensive discussions on future research directions for face re-identification.
论文关键词：Face re-identification,Surveillance facial imagery,Low-resolution,Super-resolution,Open-set matching,Deep learning,Face recognition
论文评审过程：Received 19 October 2019, Revised 7 April 2020, Accepted 4 May 2020, Available online 16 May 2020, Version of Record 13 June 2020.
论文官网地址：https://doi.org/10.1016/j.patcog.2020.107422
