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http://dx.doi.org/10.5391/JKIIS.2010.20.3.343

Image Recognition by Using Hybrid Coefficient Measure of Correlation and Distance  

Hong, Seong-Jun (School of Computer and Information Comm. Eng., Catholic Univ. of Daegu)
Cho, Yong-Hyun (School of Computer and Information Comm. Eng., Catholic Univ. of Daegu)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.20, no.3, 2010 , pp. 343-347 More about this Journal
Abstract
This paper presents an efficient image recognition method using the hybrid coefficient measure of correlation and distance. The correlation coefficient is applied to measure the statistical similarity by using Pearson coefficient, and distance coefficient is also applied to measure the spacial similarity by using city-block. The total similarity among images is calculated by extending the similarity between the feature vectors, then the feature vectors can be extracted by PCA and ICA, respectively. The proposed method has been applied to the problem for recognizing the 960(30 persons * 4 expressions * 2 lights * 4 poses) facial images of 40*50 pixels. The experimental results show that the proposed method of ICA has a superior recognition performances than the method using PCA, and is affected less by the environmental influences so as lighting.
Keywords
Correlation coefficient; Distance coefficient; Similarity measure; Image recognition; Feature extraction;
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