Face recognition on FERET face database using LDA and CCA methods

This paper provides an example of the 2D face recognition using existing LDA method and our proposed method based on CCA. LDA is a popular feature extraction technique for face recognition. Likewise, the CCA as a novel method is applied to image processing and biometrics too. CCA is a powerful multi...

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Bibliographic Details
Published in2011 34th International Conference on Telecommunications and Signal Processing pp. 570 - 574
Main Authors Jelsovka, D., Hudec, R., Breznan, M.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2011
Subjects
canonical correlation analysis CCA
Correlation
Covariance matrix
Face
Face recognition
linear discriminant analysis LDA
Training
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Summary:This paper provides an example of the 2D face recognition using existing LDA method and our proposed method based on CCA. LDA is a popular feature extraction technique for face recognition. Likewise, the CCA as a novel method is applied to image processing and biometrics too. CCA is a powerful multivariate analysis method and for that case it was applied on faces recognition. In the paper, a proposed methodology for face recognition based on information theory approach of coding and decoding the face image is presented. Developed algorithm has been tested on 20 subjects from FERET database. Test results gave a recognition rate for LDA method quite the good recognition rate 100% respectively 83% for a small number of input subjects 5 respectively 10. For a large number of inputs images is recognition rate very poor about 40% For our proposed CCA method is average recognition rate about 99% for FERET face database.
ISBN:9781457714108
1457714108
DOI:10.1109/TSP.2011.6043665