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Illumination-Invariant Face Recognition by Fusing Thermal and Visual Images via Gradient Transfer

  • Sumit AgarwalEmail author
  • Harshit S. Sikchi
  • Suparna Rooj
  • Shubhobrata Bhattacharya
  • Aurobinda Routray
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 943)

Abstract

Face recognition in real life situations like low illumination condition is still an open challenge in biometric security. It is well established that the state-of-the-art methods in face recognition provide low accuracy in the case of poor illumination. In this work, we propose an algorithm for a more robust illumination invariant face recognition using a multi-modal approach. We propose a new dataset consisting of aligned faces of thermal and visual images of a hundred subjects. We then apply face detection on thermal images using the biggest blob extraction method and apply them for fusing images of different modalities for the purpose of face recognition. An algorithm is proposed to implement fusion of thermal and visual images. We reason for why relying on only one modality can give erroneous results. We use a lighter and faster CNN model called MobileNet for the purpose of face recognition with faster inferencing and to be able to use it in real time biometric systems. We test our proposed method on our own created dataset to show that real-time face recognition on fused images shows far better results than using visual or thermal images separately.

Keywords

Biometrics Face recognition Image fusion Thermal face detection Gradient transfer MobileNet 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Sumit Agarwal
    • 1
    Email author
  • Harshit S. Sikchi
    • 1
  • Suparna Rooj
    • 1
  • Shubhobrata Bhattacharya
    • 1
  • Aurobinda Routray
    • 1
  1. 1.Indian Institute of TechnologyKharagpurIndia

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