When faces are combined with palmprints: A novel biometric fusion strategy

Guiyu Feng, Kaifeng Dong, Dewen Hu, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

39 Citations (Scopus)


This paper presents a novel fusion strategy for personal identification using face and palmprint biometrics. In the context of biometrics, three levels of information fusion schemes have been suggested: feature extraction level, matching score level and decision level. This work considers the first level fusion scheme. The purpose of our paper is to investigate whether the integration of face and palmprint biometrics can achieve higher performance that may not be possible using a single biometric indicator alone. Both Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are considered in this feature vector fusion context. We compare the results of the combined biometrics with the results of the individual face and palmprint. It is found that the performance is significantly improved in both cases, especially in the case of feature fusion using ICA obtaining encouraging results with a 99.17% recognition accuracy rate using a test set sized of 40 people.
Original languageEnglish
Pages (from-to)701-707
Number of pages7
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publication statusPublished - 1 Dec 2004

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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