Learning Unified Representations for Multi-Resolution Face Recognition
Hulingxiao He Wu Yuan Yidian Huang Shilong Zhao Wen Yuan Hanqing Li
In this work, we propose Branch-to-Trunk network (BTNet), a representation
learning method for multi-resolution face recognition. It consists of a trunk
network (TNet), namely a unified encoder, and multiple branch networks (BNets),
namely resolution adapters. As per the input, a resolution-specific BNet is
used and the output are implanted as feature maps in the feature pyramid of
TNet, at a layer with the same resolution. The discriminability of tiny faces
is significantly improved, as the interpolation error introduced by rescaling,
especially up-sampling, is mitigated on the inputs. With branch distillation
and backward-compatible training, BTNet transfers discriminative
high-resolution information to multiple branches while guaranteeing
representation compatibility. Our experiments demonstrate strong performance on
face recognition benchmarks, both for multi-resolution identity matching and
feature aggregation, with much less computation amount and parameter storage.
We establish new state-of-the-art on the challenging QMUL-SurvFace 1: N face
identification task. Our code is available at
https://github.com/StevenSmith2000/BTNet.