Adaptive Convolution Local and Global Learning for Class-Level Joint Representation of Facial Recognition with a Single Sample per Data Subject

Meng Yang, Wei Wen, Xing Wang, Linlin Shen, Guangwei Gao

Research output: Journal PublicationArticlepeer-review

10 Citations (Scopus)

Abstract

Due to the absence of training samples and intraclass variation, the extraction of discriminative facial features and construction of powerful classifiers have bottlenecks in improving the performance of facial recognition (FR) with a single sample per data subject (SSPDS). In this paper, we propose to learn regional adaptive convolution features that are locally and globally discriminative to facial identity and robust to facial variation. Then, 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 features (CJR-RACF), both discriminative facial features that are robust to facial variations and powerful representations for classification with generic facial variations have been fully exploited. Furthermore, the gallery discrimination is extracted by our proposed weight-embedded supervision in the training phase (denoted by CJR-RACFw), which is conducive to more specific features for FR with SSPDS. CJR-RACF and CJR-RACFw have been evaluated on several popular databases, including the large-scale CMU Multi-PIE, LFW, Megaface, and VGGFace datasets. Experimental results demonstrate the much higher robustness and effectiveness of the proposed methods compared to the state-of-the-art methods.

Original languageEnglish
Article number8957296
Pages (from-to)2469-2484
Number of pages16
JournalIEEE Transactions on Information Forensics and Security
Volume15
DOIs
Publication statusPublished - 2020
Externally publishedYes

Keywords

  • Face recognition
  • Image representation
  • Multi-layer neural network

ASJC Scopus subject areas

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

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