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A New Feature Extraction Method Based on the Information Fusion of Entropy Matrix and Covariance Matrix and Its Application in Face Recognition

by Shunfang Wang *,† and
School of Information Science and Engineering, Yunnan University, Kunming 650091, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Kevin H. Knuth
Entropy 2015, 17(7), 4664-4683; https://doi.org/10.3390/e17074664
Received: 3 April 2015 / Revised: 29 May 2015 / Accepted: 23 June 2015 / Published: 3 July 2015
The classic principal components analysis (PCA), kernel PCA (KPCA) and linear discriminant analysis (LDA) feature extraction methods evaluate the importance of components according to their covariance contribution, not considering the entropy contribution, which is important supplementary information for the covariance. To further improve the covariance-based methods such as PCA (or KPCA), this paper firstly proposed an entropy matrix to load the uncertainty information of random variables similar to the covariance matrix loading the variation information in PCA. Then an entropy-difference matrix was used as a weighting matrix for transforming the original training images. This entropy-difference weighting (EW) matrix not only made good use of the local information of the training samples, contrast to the global method of PCA, but also considered the category information similar to LDA idea. Then the EW method was integrated with PCA (or KPCA), to form new feature extracting method. The new method was used for face recognition with the nearest neighbor classifier. The experimental results based on the ORL and Yale databases showed that the proposed method with proper threshold parameters reached higher recognition rates than the usual PCA (or KPCA) methods. View Full-Text
Keywords: entropy matrix; entropy-difference weighting; principal components analysis; kernel principal components analysis; face recognition entropy matrix; entropy-difference weighting; principal components analysis; kernel principal components analysis; face recognition
MDPI and ACS Style

Wang, S.; Liu, P. A New Feature Extraction Method Based on the Information Fusion of Entropy Matrix and Covariance Matrix and Its Application in Face Recognition. Entropy 2015, 17, 4664-4683.

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