ICA color space for pattern recognition

Chengjun Liu, Jian Yang

Research output: Contribution to journalArticlepeer-review

68 Scopus citations


This paper presents a novel independent component analysis (ICA) color space method for pattern recognition. The novelty of the ICA color space method is twofold: 1) deriving effective color image representation based on ICA, and 2) implementing efficient color image classification using the independent color image representation and an enhanced Fisher model (EFM). First, the ICA color space method assumes that each color image is defined by three independent source images, which can be derived by means of a blind source separation procedure, such as ICA. Unlike the RGB color space, where the R, G, and B component images are correlated, the new ICA color space method derives three component images C1 C2, and C3 that are independent and hence uncorrelated. Second, the three independent color component images are concatenated to form an augmented pattern vector, whose dimensionality is reduced by principal component analysis (PCA). An EFM then derives the discriminating features of the reduced pattern vector for pattern recognition. The effectiveness of the proposed ICA color space method is demonstrated using a complex grand challenge pattern recognition problem and a large scale database. In particular, the face recognition grand challenge (FRGC) and the biometric experimentation environment (BEE) reveal that for the most challenging FRGC version 2 Experiment 4, which contains 12776 training images, 16028 controlled target images, and 8014 uncontrolled query images, the ICA color space method achieves the face verification rate (ROC III) of 73.69% at the false accept rate (FAR) of 0.1%, compared to the face verification rate (FVR) of 67.13% of the RGB color space (using the same EFM) and 11.86% of the FRGC baseline algorithm at the same FAR.

Original languageEnglish (US)
Pages (from-to)248-257
Number of pages10
JournalIEEE Transactions on Neural Networks
Issue number2
StatePublished - 2009
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


  • Biometric experimentation environment (BEE)
  • Enhanced Fisher model (EFM)
  • Face recognition grand challenge (FRGC)
  • Independent component analysis (ICA) color space
  • Pattern recognition
  • Principal component analysis (PCA)
  • RGB color space


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