Fisher’s linear discriminant (FLD) and support vector machine (SVM) in non-negative matrix factorization (NMF) residual space for face recognition

• Autorzy: Zhou C. , Wei X. , Zhang Q. , Fang X.
• Języki publikacji: EN

Abstrakty

EN

A novel method of Fisher’s linear discriminant (FLD) in the residual space is put forward for the representation of face images for face recognition, which is robust to the slight local feature changes. The residual images are computed by subtracting the reconstructed images from the original face images, and the reconstructed images are obtained by performing non-negative matrix factorization (NMF) on original images. FLD is applied to the residual images for extracting FLD subspace and the corresponding coefficient matrices. Furthermore, features are obtained by mapping the residual image to FLD subspace. Finally, the features are utilized to train and test support vector machines (SVMs) for face recognition. The computer simulation illustrates that this method is effective on the ORL database and the extended Yale face database B.

Słowa kluczowe

EN

face recognition; Fisher linear discriminant (FLD); residual image

• Wydawca: Oficyna Wydawnicza Politechniki Wrocławskiej
• Czasopismo: Optica Applicata
• Rocznik: 2010
• Tom: Vol. 40, nr 3
• Strony: 693--704
• Opis fizyczny: Bibliogr., 21 poz.

Twórcy

autor

Zhou C.

autor

Wei X.

autor

Zhang Q.

autor

Fang X.

• Key Laboratory of Advanced Design and Intelligent Computing, Dalian University, Ministry of Education, Dalian, 116622, China

Bibliografia

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