A DCNN-Based fast NIR face recognition system robust to reflected light from eyeglasses

Jeyeon Kim, Moonsoo Ra, Whoi Yul Kim

Research output: Contribution to journalArticle


Due to an increasing need for face recognition under poor lighting conditions, near infrared (NIR) face recognition based on deep convolutional neural networks (DCNN) has become an active area of research. However, in NIR face images of eyeglasses wearers, reflected light is generated around the eyes due to active NIR light sources, and it is one of the main contributors to performance degradation in NIR face recognition. In addition, there have to date been no attempts to lighten DCNN models for NIR face recognition. To solve these problems, we propose a DCNN-based fast NIR face recognition system which is robust to reflected light. This work has two main contributions: 1) We generated synthetic face images of individuals with and without eyeglasses using our proposed CycleGAN-based Glasses2Non-glasses (G2NG) data augmentation. We then constructed an augmented training database by adding the synthetic images, and the database helps to make the NIR face recognition system robust against reflected light. 2) A lightweight NIR FaceNet (LiNFNet) architecture was developed to reduce the computational complexity of the proposed system by adapting the depthwise separable convolutions and linear bottlenecks to VGGNet 16. The proposed architecture reduces the computation required, while improving the performance of NIR face recognition. Through the experiments reported in this paper, we verified that the proposed G2NG data augmentation improved the face recognition validation rate by 99.09% for NIR face images which have the reflected light from eyeglasses. Also, LiNFNet reduces the number of multiplication operations by 4.4×109 compared with VGGNet 16.

Original languageEnglish
Article number9081989
Pages (from-to)80948-80963
Number of pages16
JournalIEEE Access
StatePublished - 2020 Jan 1


  • Biometrics
  • NIR face identification
  • deep learning
  • fine-tuning
  • lightweight deep CNN

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