SeLENet: A Semi-Supervised Low Light Face Enhancement Method for Mobile Face Unlock

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

4 Scopus citations

Abstract

Facial recognition is becoming a standard feature on new smartphones. However, the face unlocking feature of devices using regular 2D camera sensors exhibits poor performance in low light environments. In this paper, we propose a semi-supervised low light face enhancement method to improve face verification performance on low light face images. The proposed method is a network with two components: decomposition and reconstruction. The decomposition component splits an input low light face image into face normals and face albedo, while the reconstruction component enhances and reconstructs the lighting condition of the input image using the spherical harmonic lighting coefficients of a direct ambient white light. The network is trained in a semi-supervised manner using both labeled synthetic data and unlabeled real data. Qualitative results demonstrate that the proposed method produces more realistic images than the state-of-the-art low light enhancement algorithms. Quantitative experiments confirm the effectiveness of our low light face enhancement method for face verification. By applying the proposed method, the gap of verification accuracy between extreme low light and neutral light face images is reduced from approximately 3% to 0.5%.

Original languageEnglish (US)
Title of host publication2019 International Conference on Biometrics, ICB 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728136400
DOIs
StatePublished - Jun 2019
Event2019 International Conference on Biometrics, ICB 2019 - Crete, Greece
Duration: Jun 4 2019Jun 7 2019

Publication series

Name2019 International Conference on Biometrics, ICB 2019

Conference

Conference2019 International Conference on Biometrics, ICB 2019
Country/TerritoryGreece
CityCrete
Period6/4/196/7/19

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Statistics, Probability and Uncertainty
  • Demography

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