A hybrid color and frequency features method for face recognition

Zhiming Liu, Chengjun Liu

Research output: Contribution to journalArticlepeer-review

73 Scopus citations

Abstract

This correspondence presents a novel hybrid Color and Frequency Features (CFF) method for face recognition. The CFF method, which applies an Enhanced Fisher Model (EFM), extracts the complementary frequency features in a new hybrid color space for improving face recognition performance. The new color space, the RIQ color space, which combines the R component image of the RGB color space and the chromatic components I and Q of the YIQ color space, displays prominent capability for improving face recognition performance due to the complementary characteristics of its component images. The EFM then extracts the complementary features from the real part, the imaginary part, and the magnitude of the R image in the frequency domain. The complementary features are then fused by means of concatenation at the feature level to derive similarity scores for classification. The complementary feature extraction and feature level fusion procedure applies to the I and Q component images as well. Experiments on the Face Recognition Grand Challenge (FRGC) version 2 Experiment 4 show that i) the hybrid color space improves face recognition performance significantly, and ii) the complementary color and frequency features further improve face recognition performance.

Original languageEnglish (US)
Pages (from-to)1975-1980
Number of pages6
JournalIEEE Transactions on Image Processing
Volume17
Issue number10
DOIs
StatePublished - 2008

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

Keywords

  • Enhanced fisher model (EFM)
  • Face recognition grand challenge (FRGC)
  • The RIQ color space

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