Learning Unified Representations for Multi-Resolution Face Recognition

• URL: http://arxiv.org/abs/2310.09563v1
• Date: Sat, 14 Oct 2023 11:26:43 GMT
• Title: Learning Unified Representations for Multi-Resolution Face Recognition
• Authors: Hulingxiao He, Wu Yuan, Yidian Huang, Shilong Zhao, Wen Yuan, Hanqing Li
• Abstract summary: Branch-to-Trunk network (BTNet) is a representation learning method for multi-resolution face recognition. Our experiments demonstrate strong performance on face recognition benchmarks, both for multi-resolution identity matching and feature aggregation, with much less amount and parameter storage.
• Score: 0.7378853859331619
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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