Regression based non-frontal face synthesis for improved identity verification

Wong, Yong, Sanderson, Conrad and Lovell, Brian C. (2009). Regression based non-frontal face synthesis for improved identity verification. In: Xiaoyi Jiang and Nicolai Petkov, Computer Analysis of Images and Patterns. 13th International Conference, CAIP 2009, Munster, Germany, (116-124). 2-4 September, 2009. doi:10.1007/978-3-642-03767-2_14

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Author Wong, Yong
Sanderson, Conrad
Lovell, Brian C.
Title of paper Regression based non-frontal face synthesis for improved identity verification
Conference name 13th International Conference, CAIP 2009
Conference location Munster, Germany
Conference dates 2-4 September, 2009
Proceedings title Computer Analysis of Images and Patterns   Check publisher's open access policy
Journal name Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)   Check publisher's open access policy
Place of Publication Heidelberg, Germany
Publisher Springer-Verlag
Publication Year 2009
Year available 2009
Sub-type Fully published paper
DOI 10.1007/978-3-642-03767-2_14
ISBN 978-3-642-03766-5
ISSN 0302-9743
1611-3349
Editor Xiaoyi Jiang
Nicolai Petkov
Volume 5702 LNCS
Start page 116
End page 124
Total pages 9
Language eng
Abstract/Summary We propose a low-complexity face synthesis technique which transforms a 2D frontal view image into views at specific poses, without recourse to computationally expensive 3D analysis or iterative fitting techniques that may fail to converge. The method first divides a given image into multiple overlapping blocks, followed by synthesising a non-frontal representation through applying a multivariate linear regression model on a low-dimensional representation of each block. To demonstrate one application of the proposed technique, we augment a frontal face verification system by incorporating multi-view reference (gallery) images synthesised from the frontal view. Experiments on the pose subset of the FERET database show considerable reductions in error rates, especially for large deviations from the frontal view.
Subjects 230204 Applied Statistics
280207 Pattern Recognition
280208 Computer Vision
280203 Image Processing
E1
010401 Applied Statistics
080104 Computer Vision
080106 Image Processing
080109 Pattern Recognition and Data Mining
970108 Expanding Knowledge in the Information and Computing Sciences
Keyword Pattern recognition
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Wed, 02 Sep 2009, 21:29:10 EST by Conrad Sanderson on behalf of School of Information Technol and Elec Engineering