March 14, 2024, 4:45 a.m. | Zhonglin Sun, Chen Feng, Ioannis Patras, Georgios Tzimiropoulos

cs.CV updates on

arXiv:2403.08161v1 Announce Type: new
Abstract: In this work we focus on learning facial representations that can be adapted to train effective face recognition models, particularly in the absence of labels. Firstly, compared with existing labelled face datasets, a vastly larger magnitude of unlabeled faces exists in the real world. We explore the learning strategy of these unlabeled facial images through self-supervised pretraining to transfer generalized face recognition performance. Moreover, motivated by one recent finding, that is, the face saliency area …

abstract arxiv datasets explore face face recognition focus labels landmark recognition self-supervised learning supervised learning train type work world

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