Iterative dynamic generic learning for single sample face recognition with a contaminated gallery

Meng Pang, Yiu Ming CHEUNG, Qiquan Shi, Mengke Li

Research output: Chapter in book/report/conference proceedingConference contributionpeer-review

Abstract

This paper studies a new challenging problem in face recognition (FR) with single sample per person (SSPP), i.e., SSPP FR with a contaminated gallery (SSPP-CG FR), where the gallery is contaminated by variations. In SSPP-CG FR, the popular generic learning methods will suffer serious performance degradation because the applied prototype plus variation (P+V) model is not suitable in such scenarios. The reasons are twofold: 1) The contaminated gallery samples yield bad prototypes to represent the persons; 2) The generated variation dictionary is simply based on the subtraction of average face from generic samples of the same person and cannot well depict the intra-personal variations. To tackle SSPPCG FR, we propose a novel Iterative Dynamic Generic Learning (IDGL) method, where the labeled gallery and unlabeled query sets are fed into a dynamic label feedback network for learning. Specifically, IDGL first recovers the prototypes via a semi-supervised low-rank representation (SSLRR) framework and learns a representative variation dictionary by extracting the 'sample-specific' corruptions from an auxiliary generic set. Then, it puts them into the P+V model to estimate labels for query samples. Subsequently, the estimated labels are used as the feedbacks to modify the SSLRR, thus updating new prototypes for the next round of P+V based label estimation. With the dynamic learning network, the accuracy of the estimated labels is improved iteratively owing to the steadily enhanced prototypes. Experiments on various benchmark databases have verified the superiority of IDGL.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Multimedia and Expo, ICME 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728113319
DOIs
Publication statusPublished - Jul 2020
Event2020 IEEE International Conference on Multimedia and Expo, ICME 2020 - London, United Kingdom
Duration: 6 Jul 202010 Jul 2020

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
Volume2020-July
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Conference

Conference2020 IEEE International Conference on Multimedia and Expo, ICME 2020
Country/TerritoryUnited Kingdom
CityLondon
Period6/07/2010/07/20

Scopus Subject Areas

  • Computer Networks and Communications
  • Computer Science Applications

User-Defined Keywords

  • Contaminated gallery set
  • Face recognition
  • Low-rank representation
  • Single sample per person

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