Adaptive convolution local and global learning for class-level joint representation of face recognition with single sample per person

Wei Wen, Xing Wang, Linlin Shen, Meng Yang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

Due to the absence of samples with intra-class variation, extracting discriminative facial features and building powerful classifiers are the bottlenecks of improving the performance of face recognition (FR) with single sample per person (SSPP). In this paper, we propose to learn regional adaptive convolution features which are locally and globally discriminative to face identity and robust to face variation. With collected generic facial variations, a novel class-level joint representation framework is presented to exploit the distinctiveness and class-level commonality of different facial features. In the proposed class-level joint representation with regional adaptive convolution feature (CJR-RACF), both discriminative facial features robust to various facial variations and powerful representation for classification with generic facial variations that can overcome the small-sample-size problem are fully exploited. CJR-RACF has been evaluated on several popular databases, including large-scale CMU Multi-PIE and LFW databases. Experimental results demonstrate the much higher robustness and effectiveness of CJR-RACF to complex facial variations compared to the state-of-the-art methods.

Original languageEnglish
Title of host publication2018 24th International Conference on Pattern Recognition, ICPR 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3537-3542
Number of pages6
ISBN (Electronic)9781538637883
DOIs
Publication statusPublished - 26 Nov 2018
Externally publishedYes
Event24th International Conference on Pattern Recognition, ICPR 2018 - Beijing, China
Duration: 20 Aug 201824 Aug 2018

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2018-August
ISSN (Print)1051-4651

Conference

Conference24th International Conference on Pattern Recognition, ICPR 2018
Country/TerritoryChina
CityBeijing
Period20/08/1824/08/18

Keywords

  • class-level joint representation
  • face recognition
  • single sample per person

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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