Learning Facial Representations from the Cycle-consistency of Face

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

Faces manifest large variations in many aspects, such as identity, expression, pose, and face styling. Therefore, it is a great challenge to disentangle and extract these characteristics from facial images, especially in an unsupervised manner. In this work, we introduce cycle-consistency in facial characteristics as free supervisory signal to learn facial representations from unlabeled facial images. The learning is realized by superimposing the facial motion cycle-consistency and identity cycle-consistency constraints. The main idea of the facial motion cycle-consistency is that, given a face with expression, we can perform de-expression to a neutral face via the removal of facial motion and further perform re-expression to reconstruct back to the original face. The main idea of the identity cycle-consistency is to exploit both de-identity into mean face by depriving the given neutral face of its identity via feature re-normalization and re-identity into neutral face by adding the personal attributes to the mean face. At training time, our model learns to disentangle two distinct facial representations to be useful for performing cycle-consistent face reconstruction. At test time, we use the linear protocol scheme for evaluating facial representations on various tasks, including facial expression recognition and head pose regression. We also can directly apply the learnt facial representations to person recognition, frontalization and image-to-image translation. Our experiments show that the results of our approach is competitive with those of existing methods, demonstrating the rich and unique information embedded in the disentangled representations. Code is available at https://github.com/JiaRenChang/FaceCycle.

原文English
主出版物標題Proceedings - 2021 IEEE/CVF International Conference on Computer Vision, ICCV 2021
發行者Institute of Electrical and Electronics Engineers Inc.
頁面9660-9669
頁數10
ISBN(電子)9781665428125
DOIs
出版狀態Published - 2021
事件18th IEEE/CVF International Conference on Computer Vision, ICCV 2021 - Virtual, Online, Canada
持續時間: 11 10月 202117 10月 2021

出版系列

名字Proceedings of the IEEE International Conference on Computer Vision
ISSN(列印)1550-5499

Conference

Conference18th IEEE/CVF International Conference on Computer Vision, ICCV 2021
國家/地區Canada
城市Virtual, Online
期間11/10/2117/10/21

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