TY - JOUR
T1 - ICA color space for pattern recognition
AU - Liu, Chengjun
AU - Yang, Jian
N1 - Funding Information:
Manuscript received July 05, 2007; revised February 26, 2008 and June 04, 2008; accepted August 24, 2008. First published January 06, 2009; current version published February 06, 2009. This work was supported in part by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice, under Grants 2006-IJ-CX-K033 and 2007-RG-CX-K011.
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Biometric experimentation environment (BEE)
KW - Enhanced Fisher model (EFM)
KW - Face recognition grand challenge (FRGC)
KW - Independent component analysis (ICA) color space
KW - Pattern recognition
KW - Principal component analysis (PCA)
KW - RGB color space
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U2 - 10.1109/TNN.2008.2005495
DO - 10.1109/TNN.2008.2005495
M3 - Article
C2 - 19150790
AN - SCOPUS:60849115543
SN - 2162-237X
VL - 20
SP - 248
EP - 257
JO - IEEE Transactions on Neural Networks
JF - IEEE Transactions on Neural Networks
IS - 2
ER -