TY - CHAP
T1 - New Color Features for Pattern Recognition
AU - Liu, Chengjun
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - This chapter presents a pattern recognition framework that applies new color features, which are derived from both the primary color (the red component) and the subtraction of the primary colors (the red minus green component, the blue minus green component). In particular, feature extraction from the three color components consists of the following processes: Discrete Cosine Transform (DCT) for dimensionality reduction for each of the three color components, concatenation of the DCT features to form an augmented feature vector, and discriminant analysis of the augmented feature vector with enhanced generalization performance. A new similarity measure is presented to further improve pattern recognition performance of the pattern recognition framework. Experiments using a large scale, grand challenge pattern recognition problem, the Face Recognition Grand Challenge (FRGC), show the feasibility of the proposed framework. Specifically, the experimental results on the most challenging FRGC version 2 Experiment 4 with 36,818 color images reveal that the proposed framework helps improve face recognition performance, and the proposed new similarity measure consistently performs better than other popular similarity measures.
AB - This chapter presents a pattern recognition framework that applies new color features, which are derived from both the primary color (the red component) and the subtraction of the primary colors (the red minus green component, the blue minus green component). In particular, feature extraction from the three color components consists of the following processes: Discrete Cosine Transform (DCT) for dimensionality reduction for each of the three color components, concatenation of the DCT features to form an augmented feature vector, and discriminant analysis of the augmented feature vector with enhanced generalization performance. A new similarity measure is presented to further improve pattern recognition performance of the pattern recognition framework. Experiments using a large scale, grand challenge pattern recognition problem, the Face Recognition Grand Challenge (FRGC), show the feasibility of the proposed framework. Specifically, the experimental results on the most challenging FRGC version 2 Experiment 4 with 36,818 color images reveal that the proposed framework helps improve face recognition performance, and the proposed new similarity measure consistently performs better than other popular similarity measures.
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U2 - 10.1007/978-3-642-28457-1_2
DO - 10.1007/978-3-642-28457-1_2
M3 - Chapter
AN - SCOPUS:84885593102
SN - 9783642284564
T3 - Intelligent Systems Reference Library
SP - 15
EP - 34
BT - Cross Disciplinary Biometric Systems
A2 - Liu, Chengjun
A2 - Mago, Vijay Kumar
ER -