By Frank Lin, Clinton Fookes, Vinod Chandran, Sridha Sridharan (auth.), Seong-Whan Lee, Stan Z. Li (eds.)
Many functions in government,airport, advertisement, protection and legislations enfor- mentareashaveabasicneedforautomaticauthenticationofhumansbothlocally orremotelyonaroutinebasis.Thedemandforautomaticauthenticationsystems utilizing biometrics, together with face, ?ngerprint, gait, and iris, has been expanding in lots of features of existence. the aim of the 2007 overseas convention on Biometrics (ICB 2007) used to be to supply a platform for researchers, engineers, s- tem architects and architects to record fresh advances and trade rules within the region of biometrics and similar applied sciences. ICB 2007 bought various high quality study papers. In all 303 papers have been submitted from 29 nations worldwide. of those 34 papers have been accredited for oral presentation and ninety one papers have been permitted for poster presentation. this system consisted of 7 oral classes, 3 poster periods, instructional periods, and 4 keynote speeches on quite a few themes on biometrics. we wish to thank all of the authors who submitted their manuscripts to the convention, and the entire individuals of this system Committee and reviewers who spent worthwhile time delivering reviews on each one paper. we wish to thank the convention administrator and secretariat for making the convention profitable. We additionally desire to recognize the IEEE, IAPR, Korea details technological know-how Society, Korea collage, Korea college BK21 software program study department, KoreaScience and EngineeringFoundation,KoreaUniversity Institute of desktop, details and verbal exchange, Korea Biometrics organization, Lumidigm Inc., Ministry of data and verbal exchange Republic of Korea, and Springer for sponsoring and assisting this convention. August 2007 Seong-Whan Lee Stan Z. Li association ICB 2007 used to be prepared via heart for Arti?cial imaginative and prescient learn, Korea University.
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Extra info for Advances in Biometrics: International Conference, ICB 2007, Seoul, Korea, August 27-29, 2007. Proceedings
A(N ) are ﬁxed. Then Eq. (4) is reduced to a quadratic expression of A(n) , consisting of orthonormal columns. We have G(A(n) ) = K(n) ×n A(n) 2 = A(n) K (n) 2 , where K(n) = X ×1 A(1) · · · ×n−1 A(n−1) ×n+1 A(n+1) · · · ×N AN , (5) Color Face Tensor Factorization and Slicing 23 Table 1. Tucker decomposition explains PCA, 2D-PCA, GLRAM, CSA, and HOSVD in general framework. Assume the last mode is associated with samples. The grayscale face image ensemble constructs the 3-way tensor(rows, columns, samples) and color face image ensemble does the 4-way tensor(rows, columns, color, samples).
In order to detect faces in the input image, conventional face detection algorithms have been carried out by scanning all possible analysis windows. In addition, to ﬁnd faces of various size, the image is repeatedly down-scaled with a pre-deﬁned scaling factor. This is done until the scaled image is smaller than the sub window size. Although face detection by this full-search algorithm shows optimal performance, it is computationally expensive and slow. To solve this problem, we propose diﬀerence of pyramid(DoP) images coupled with two-dimensional logarithmic search.
Thus the third slice S :,:,3,i4 of an intermediate feature tensor is a ﬁnal illumination-robust feature matrix. In the ﬁnal feature matrix, information of X :,:,:,i4 about variations on rows and columns are retained but on color are minimized. The illumination-robust feature extraction for a test color face tensor Y ∈ RI1 ×I2 ×3 is summarized to (3) T = (Y − M) ×1 A(1) ×2 A(1) ×3 a3 (3) where a3 4 , (8) is the third column vector of A(3) . Numerical Experiments Our MATLAB implementation of the CFTFS partly uses the tensor toolbox .