TY - JOUR
T1 - Fusion of the complementary Discrete Cosine Features in the YIQ color space for face recognition
AU - Liu, Zhiming
AU - Liu, Chengjun
N1 - Funding Information:
The authors thank the anonymous reviewers for their critical and constructive comments and suggestions. This work was partially supported by the Grants 2006-IJ-CX-K033 and 2007-RG-CX-K011 awarded by the National Institute of Justice, Office of Justice Programs, US Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect those of the Department of Justice.
PY - 2008/9
Y1 - 2008/9
N2 - This paper presents a novel Discrete Cosine Features (DCF) method for face recognition. The DCF method works by fusing the complementary features derived from the Discrete Cosine Transform (DCT) of the color component images in the YIQ color space. The novelty of the DCF method thus comes from both the multiple imaging (three component images) in the YIQ color space, and the multiple face encoding (different masking) in the DCT frequency domain. First, each color component image in the YIQ color space is transformed to the frequency domain via DCT, where three DCT frequency sets are derived by means of masking to encode the image at different representation levels (the reconstructed images display different details). Second, the three DCT frequency sets at the same representation level across the Y, I, and Q color component images are concatenated-the feature level fusion-to form an augmented pattern vector. Third, the complementary features from each of the three augmented pattern vectors (corresponding to the three different representation levels) are extracted using an Enhanced Fisher Model (EFM). Finally, the three similarity matrices generated using the complementary features are fused by means of the sum rule-the decision level fusion-to derive the final similarity matrix for face recognition. The effectiveness of the proposed DCF method is demonstrated using a complex grand challenge face recognition problem and a large scale database. In particular, the Face Recognition Grand Challenge (FRGC) and the Biometric Experimentation Environment (BEE) show that for the most challenging FRGC version 2 Experiment 4, which contains 12,776 training images, 16,028 controlled target images, and 8014 uncontrolled query images, the DCF method achieves the face verification rate (ROC III) of 81.34% at the false accept rate of 0.1%, compared to the FRGC baseline algorithm face verification rate of 11.86% at the same false accept rate.
AB - This paper presents a novel Discrete Cosine Features (DCF) method for face recognition. The DCF method works by fusing the complementary features derived from the Discrete Cosine Transform (DCT) of the color component images in the YIQ color space. The novelty of the DCF method thus comes from both the multiple imaging (three component images) in the YIQ color space, and the multiple face encoding (different masking) in the DCT frequency domain. First, each color component image in the YIQ color space is transformed to the frequency domain via DCT, where three DCT frequency sets are derived by means of masking to encode the image at different representation levels (the reconstructed images display different details). Second, the three DCT frequency sets at the same representation level across the Y, I, and Q color component images are concatenated-the feature level fusion-to form an augmented pattern vector. Third, the complementary features from each of the three augmented pattern vectors (corresponding to the three different representation levels) are extracted using an Enhanced Fisher Model (EFM). Finally, the three similarity matrices generated using the complementary features are fused by means of the sum rule-the decision level fusion-to derive the final similarity matrix for face recognition. The effectiveness of the proposed DCF method is demonstrated using a complex grand challenge face recognition problem and a large scale database. In particular, the Face Recognition Grand Challenge (FRGC) and the Biometric Experimentation Environment (BEE) show that for the most challenging FRGC version 2 Experiment 4, which contains 12,776 training images, 16,028 controlled target images, and 8014 uncontrolled query images, the DCF method achieves the face verification rate (ROC III) of 81.34% at the false accept rate of 0.1%, compared to the FRGC baseline algorithm face verification rate of 11.86% at the same false accept rate.
KW - Decision level fusion
KW - Discrete Cosine Features (DCF) method
KW - Discrete Cosine Transform (DCT)
KW - Face Recognition Grand Challenge (FRGC)
KW - Face recognition
KW - Feature level fusion
KW - Multiple face encoding
KW - Multiple imaging
KW - YIQ color space
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U2 - 10.1016/j.cviu.2007.12.002
DO - 10.1016/j.cviu.2007.12.002
M3 - Article
AN - SCOPUS:50349093523
SN - 1077-3142
VL - 111
SP - 249
EP - 262
JO - Computer Vision and Image Understanding
JF - Computer Vision and Image Understanding
IS - 3
ER -