On Intelligent Surveillance Systems and Face Recognition for Mass Transport Security

Lovell, Brian C., Chen, Shaokang, Bigdeli, Abbas, Berglund, Erik and Sanderson, Conrad (2008). On Intelligent Surveillance Systems and Face Recognition for Mass Transport Security. In: International Conference on Control, Automation, Robotics and Vision (ICARCV), 2008., Hanoi, Vietnam, (713-718). 17 - 20 December 2008. doi:10.1109/ICARCV.2008.4795605


Author Lovell, Brian C.
Chen, Shaokang
Bigdeli, Abbas
Berglund, Erik
Sanderson, Conrad
Title of paper On Intelligent Surveillance Systems and Face Recognition for Mass Transport Security
Conference name International Conference on Control, Automation, Robotics and Vision (ICARCV), 2008.
Conference location Hanoi, Vietnam
Conference dates 17 - 20 December 2008
Journal name 2008 10th International Conference on Control, Automation, Robotics and Vision, ICARCV 2008
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2008
Year available 2008
Sub-type Fully published paper
DOI 10.1109/ICARCV.2008.4795605
ISBN 9781424422869
Start page 713
End page 718
Total pages 6
Collection year 2009
Language eng
Abstract/Summary We describe a project to trial and develop enhanced surveillance technologies for public safety. A key technology is robust recognition of faces from low-resolution CCTV footage where there may be as few as 12 pixels between the eyes. Current commercial face recognition systems require 60-90 pixels between the eyes as well as tightly controlled image capture conditions. Our group has thus concentrated on fundamental face recognition issues such as robustness to low resolution and image capture conditions as required for uncontrolled CCTV surveillance. In this paper, we propose a fast multi-class pattern classification approach to enhance PCA and FLD methods for 2D face recognition under changes in pose, illumination, and expression. The method first finds the optimal weights of features pairwise and constructs a feature chain in order to determine the weights for all features. Computational load of the proposed approach is extremely low by design, in order to facilitate usage in automated surveillance. The method is evaluated on PIE, FERET, and Asian Face databases, with the results showing that the method performs remarkably well compared to several benchmark appearance-based methods. Moreover, the method can reliably recognise faces with large pose angles from just one gallery image.
Subjects 280203 Image Processing
280207 Pattern Recognition
280208 Computer Vision
Q-Index Code E1
Q-Index Status Provisional Code
Institutional Status UQ

 
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Created: Tue, 28 Apr 2009, 19:01:06 EST by Dr Ildiko Horvath on behalf of School of Information Technol and Elec Engineering