Local classifier chains for deep face recognition

Lingfeng Zhang, Ioannis A. Kakadiaris

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


This paper focuses on improving the performance of current convolutional neural networks in face recognition without changing the network architecture. We propose a hierarchical framework that builds chains of local binary neural networks after one global neural network over all the class labels, Local Classifier Chains based Convolutional Neural Networks (LCC-CNN). Two different criteria based on a similarity matrix and confusion matrix are introduced to select binary label pairs to create local deep networks. To avoid error propagation, each testing sample travels through one global model and a local classifier chain to obtain its final prediction. The proposed framework has been evaluated with UHDB31 and CASIA-WebFace datasets. The experimental results indicate that our framework achieves better performance when compared with using only baseline methods as the global deep network. The accuracy is improved by 2.7% and 0.7% on the two datasets, respectively.

Original languageEnglish (US)
Title of host publicationIEEE International Joint Conference on Biometrics, IJCB 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781538611241
StatePublished - Jan 29 2018
Event2017 IEEE International Joint Conference on Biometrics, IJCB 2017 - Denver, United States
Duration: Oct 1 2017Oct 4 2017

Publication series

NameIEEE International Joint Conference on Biometrics, IJCB 2017


Conference2017 IEEE International Joint Conference on Biometrics, IJCB 2017
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Instrumentation
  • Signal Processing
  • Biomedical Engineering


Dive into the research topics of 'Local classifier chains for deep face recognition'. Together they form a unique fingerprint.

Cite this