Cascading Trilinear Tensors for Face Authentication

Author

Wagner, Gregory M. ; Sinzinger, Eric D.

Author_Institution

Dept. of Comput. Sci., Texas Tech Univ. Lubbock, Lubbock, TX

fYear

2008

fDate

7-9 Jan. 2008

Firstpage

1

Lastpage

6

Abstract

This paper presents a method to improve the accuracy rates of face authentication between images with different poses. Trilinear tensors are used to adjust the pose of the training and testing images. All the images are transformed by a pose adjustment algorithm so novel images are generated that have the same pose. These novel images are then used to train and test support vector machine (SVM) face authentication functions to verify the identity of the people in the images. The overall results show that the accuracy improves when the poses of the images are adjusted.

Keywords

face recognition; learning (artificial intelligence); pose estimation; support vector machines; tensors; SVM face authentication functions; cascading trilinear tensors; people identity verification; pose adjustment algorithm; support vector machine; testing images; training images; Authentication; Cameras; Charge-coupled image sensors; Image generation; Kernel; Pixel; Support vector machine classification; Support vector machines; Tensile stress; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Applications of Computer Vision, 2008. WACV 2008. IEEE Workshop on

Conference_Location

Copper Mountain, CO

ISSN

1550-5790

Print_ISBN

978-1-4244-1913-5

Electronic_ISBN

1550-5790

Type

conf

DOI

10.1109/WACV.2008.4544029

Filename

4544029