Publication Type

Journal Article

Version

acceptedVersion

Publication Date

6-2021

Abstract

With superhuman-level performance of face recognition, we are more concerned about the recognition of fine-grained attributes, such as emotion, age, and gender. However, given that the label space is extremely large and follows a long-tail distribution, it is quite expensive to collect sufficient samples for fine-grained attributes. This results in imbalanced training samples and inferior attribute recognition models. To this end, we propose the use of arbitrary attribute combinations, without human effort, to synthesize face images. In particular, to bridge the semantic gap between high-level attribute label space and low-level face image, we propose a novel neural-network-based approach that maps the target attribute labels to an embedding vector, which can be fed into a pretrained image decoder to synthesize a new face image. Furthermore, to regularize the attribute for image synthesis, we propose to use a perceptual loss to make the new image explicitly faithful to target attributes. Experimental results show that our approach can generate photorealistic face images from attribute labels, and more importantly, by serving as augmented training samples, these images can significantly boost the performance of attribute recognition model. The code is open-sourced at this link.

Keywords

Face, Face recognition, Image recognition, Image reconstruction, Task analysis, Gallium nitride, Decoding

Discipline

Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Neural Networks and Learning Systems

Volume

32

Issue

6

First Page

2733

Last Page

2743

ISSN

2162-2388

Identifier

10.1109/TNNLS.2020.3007790

Publisher

Institute of Electrical and Electronics Engineers

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TNNLS.2020.3007790

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