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RGB-D Face Recognition: A Comparative Study of Representative Fusion Schemes

  • Jiyun Cui
  • Hu Han
  • Shiguang Shan
  • Xilin Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)

Abstract

RGB-D face recognition (FR) has drawn increasing attention in recent years with the advances of new RGB-D sensing technologies, and the decrease in sensor price. While a number of multi-modality fusion methods are available in face recognition, there is not known conclusion how the RGB and depth should be fused. We provide a comparative study of four representative fusion schemes in RGB-D face recognition, covering signal-level, feature-level, score-level fusions, and a hybrid fusion we designed for RGB-D face recognition. The proposed method achieves state-of-the-art performance on two large RGB-D datasets. A number of insights are provided based on the experimental evaluations.

Keywords

RGB-D face recognition Signal-level fusion Feature-level fusion Score-level fusion Hybrid fusion 

Notes

Acknowledgement

This research was supported in part by the Natural Science Foundation of China (grants 61732004, and 61672496), External Cooperation Program of Chinese Academy of Sciences (CAS) (grant GJHZ1843), and Youth Innovation Promotion Association CAS (2018135).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Jiyun Cui
    • 1
    • 2
  • Hu Han
    • 1
  • Shiguang Shan
    • 1
    • 2
  • Xilin Chen
    • 1
    • 2
  1. 1.Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS)Institute of Computing Technology, CASBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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