Research on face recognition method based on deep learning in natural environment

Jiali Yan, Longfei Zhang, Yufeng Wu, Penghui Guo, Fuquan Zhang, Shuo Tang, Gangyi Ding, Fuquan Zheng, Lin Xu

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

6 Citations (Scopus)

Abstract

In the present study, there are a number of recognition methods with high recognition accuracy, which are based on deep learning. However, these methods usually have a good effect in a restricted environment, but in the natural environment, the accuracy of face recognition has decreased significantly, especially in the case of occlusion, face recognition will appear inaccurate or unrecognized situation. Based on this, this paper presents a face recognition method based on the deep learning in the natural environment, hoping to achieve robust performance in the natural environment, especially in the case of occlusion. The main contribution of this paper is improving the method of multi-patches by using 4 areas' patches in the face. And in order to have a higher performance, we use a Joint Bayesian (JB) measure in face-verification. Finally, we trained the model by the set of CASIA-WebFace and test it in the Labeled Faces in the Wild (LFW).

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages501-506
Number of pages6
ISBN (Electronic)9781538629659
DOIs
Publication statusPublished - 1 Jul 2017
Event8th IEEE International Conference on Awareness Science and Technology, iCAST 2017 - Taichung, Taiwan, Province of China
Duration: 8 Nov 201710 Nov 2017

Publication series

NameProceedings - 2017 IEEE 8th International Conference on Awareness Science and Technology, iCAST 2017
Volume2018-January

Conference

Conference8th IEEE International Conference on Awareness Science and Technology, iCAST 2017
Country/TerritoryTaiwan, Province of China
CityTaichung
Period8/11/1710/11/17

Keywords

  • Joint Bayesian
  • Natural environment
  • face recognition
  • multipatches

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