Face recognition based on spectroface and uniform eigen-space SVD for one training image per person

Abstract

This paper proposes a method based on the first spectroface and singular value decomposition (SVD) to deal with face recognition with one training image per person. To acquire more information from the single training sample, the first order spectroface method is applied to obtain spectroface representation of facial image, then the spectroface representation is projected onto a uniform eigen-space that is obtained from SVD of standard spectroface image and the resultant coefficient vector is used as the feature of the facial image for recognition. The L1 distance classifier is adopted in recognition. Two standard databases from Yale University and Olivetti Research Laboratory are selected to evaluate the recognition accuracy of the proposed method. Experimental results show the effectiveness of the presented method.

Adaptive hybrid technique for face recognition

“…Singular Value Decompositions (SVDs) are define as an algebraic practice to factor several rectangular matrixs for the product of three other matrices. If is identify as a gray level facial image and , after that there are two orthogonal matrices and diagonal matrix such that where is the eigenvalue of in addition to , is Singular of Value (SV) from facial pic A, , are pillar of eigenvectors of and corresponding to eigenvalue , respectively [9][10][11]. SVD has important properties and these are verified in [12].…”