Multi-feature fusion for thermal face recognition

Yin Bi, Mingsong Lv, Yangjie Wei, Nan Guan, Wang Yi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)


Human face recognition has been researched for the last three decades. Face recognition with thermal images now attracts significant attention since they can be used in low/none illuminated environment. However, thermal face recognition performance is still insufficient for practical applications. One main reason is that most existing work leverage only single feature to characterize a face in a thermal image. To solve the problem, we propose multi-feature fusion, a technique that combines multiple features in thermal face characterization and recognition. In this work, we designed a systematical way to combine four features, including Local binary pattern, Gabor jet descriptor, Weber local descriptor and Down-sampling feature. Experimental results show that our approach outperforms methods that leverage only a single feature and is robust to noise, occlusion, expression, low resolution and differentl1-minimization methods.
Original languageEnglish
Pages (from-to)366-374
Number of pages9
JournalInfrared Physics and Technology
Publication statusPublished - 1 Jul 2016
Externally publishedYes


  • Feature fusion
  • Sparse representation
  • Thermal face recognition

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Condensed Matter Physics

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