Title :
Fractional Eigenfaces
Author :
de Carvalho, T.B.A. ; Sibaldo, M.A.A. ; Tsang, I.R. ; Cavalcanti, G.D.C. ; Tsang, I.J. ; Sijbers, J.
Author_Institution :
Unidade Academica de Garanhuns, Univ. Fed. Rural de Pernambuco, Garanhuns, Brazil
Abstract :
The proposed Fractional Eigenfaces method is a feature extraction technique for high dimensional data. It is related to Fractional PCA (FPCA), which is based on the theory of fractional covariance matrix, and it is an extension of the classical Eigenfaces. Like FPCA, it computes projections for a low dimensional space from the fractional covariance matrix and similar to the Eigenfaces, it is suited for high dimensional data. Moreover, the proposed technique extends the fractional transformation of the data for more stages of the feature extractions than FPCA. The Fractional Eigenfaces is evaluated in three different face databases. Results show that it achieves a higher accuracy rate than FPCA and Eigenfaces according to the Wilcoxon hypothesis test.
Keywords :
covariance matrices; face recognition; feature extraction; principal component analysis; visual databases; FPCA; Wilcoxon hypothesis test; feature extraction technique; fractional PCA; fractional covariance matrix; fractional data transformation; fractional eigenfaces; high dimensional data; low dimensional space; principal component analysis; Accuracy; Covariance matrices; Equations; Face; Feature extraction; Mathematical model; Principal component analysis; Dimensionality reduction; Face recognition; Fractional covariance matrix; Principal component analysis;
Conference_Titel :
Image Processing (ICIP), 2014 IEEE International Conference on
Conference_Location :
Paris
DOI :
10.1109/ICIP.2014.7025051
