FPCode: An efficient approach for multi-modal biometrics

Linlin Shen, Li Bai, Zhen Ji

Research output: Journal PublicationArticlepeer-review

16 Citations (Scopus)


Although face recognition technology has progressed substantially, its performance is still not satisfactory due to the challenges of great variations in illumination, expression and occlusion. This paper aims to improve the accuracy of personal identification, when only few samples are registered as templates, by integrating multiple modal biometrics, i.e. face and palmprint. We developed in this paper a feature code, namely FPCode, to represent the features of both face and palmprint. Though feature code has been used for palmprint recognition in literature, it is first applied in this paper for face recognition and multi-modal biometrics. As the same feature is used, fusion is much easier. Experimental results show that both feature level and decision level fusion strategies achieve much better performance than single modal biometrics. The proposed approach uses fixed length 1/0 bits coding scheme that is very efficient in matching, and at the same time achieves higher accuracy than other fusion methods available in literature.

Original languageEnglish
Pages (from-to)273-286
Number of pages14
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Issue number2
Publication statusPublished - Mar 2011
Externally publishedYes


  • Face recognition
  • feature fusion
  • fusion code
  • Gabor feature
  • palmprint recognition

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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