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
this https URL

提出了 Branch-to-Trunk 网络（BTNet），一种用于多分辨率人脸识别的表示学习方法，通过统一编码器的主干网络和多个分辨率适配器的分支网络组成，提高了微小人脸的可辨识性，同时在输入上缓解了由缩放引入的插值误差，通过分支蒸馏和向后兼容的训练，在保证表示的兼容性的前提下，将有辨别力的高分辨率信息传递给多个分支。在人脸识别基准测试中表现出较强的性能，并且计算量和参数存储量明显减少，同时在具有挑战性的 QMUL-SurvFace 1：N 人脸识别任务中建立了新的最优结果。