VD-GAN: A Unified Framework for Joint Prototype and Representation Learning from Contaminated Single Sample per Person

Meng Pang, Binghui Wang, Yiu Ming CHEUNG, Yiran Chen, Bihan Wen*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Single sample per person (SSPP) face recognition with a ${c}$ ontaminated biometric ${e}$ nrolment database (SSPP-ce FR) is an emerging practical FR problem, where the SSPP in the enrolment database is no longer standard but contaminated by nuisance facial variations such as expression, lighting, pose, and disguise. In this case, the conventional SSPP FR methods, including the patch-based and generic learning methods, will suffer from serious performance degradation. Few recent methods were proposed to tackle SSPP-ce FR by either performing prototype learning on the contaminated enrolment database or learning discriminative representations that are robust against variation. Despite that, most of these approaches can only handle a specified single variation, e.g., pose, but cannot be extended to multiple variations. To address these two limitations, we propose a novel Variation Disentangling Generative Adversarial Network (VD-GAN) to jointly perform prototype learning and representation learning in a unified framework. The proposed VD-GAN consists of an encoder-decoder structural generator and a multi-task discriminator to handle universal variations including single, multiple, and even mixed variations in practice. The generator and discriminator play an adversarial game such that the generator learns a discriminative identity representation and generates an identity-preserved prototype for each face image, while the discriminator aims to predict face identity label, distinguish real vs. fake prototype, and disentangle target variations from the learned representations. Qualitative and quantitative evaluations on various real-world face datasets containing single/multiple and mixed variations demonstrate the effectiveness of VD-GAN.

Original languageEnglish
Article number9316985
Pages (from-to)2246-2259
Number of pages14
JournalIEEE Transactions on Information Forensics and Security
Volume16
DOIs
Publication statusPublished - 2021

Scopus Subject Areas

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications

User-Defined Keywords

  • generative adversarial network
  • prototype learning
  • representation learning
  • Single sample per person

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