Experimental analysis of face recognition on still and CCTV images

Chen, Shaokang, Berglund, Erik, Bigdeli, Abbas, Sanderson, Conrad and Lovell, Brian C. (2008). Experimental analysis of face recognition on still and CCTV images. In: I. Paulidis and I. Kakadiaris, Advanced Video and Signal Based Surveillance 2008. IEEE International Conference on Advanced Video and Signal Based Surveillance 2008 (AVSS '08), Santa Fe, New Mexico, U.S.A., (317-324). 1-3 September 2008. doi:10.1109/AVSS.2008.15

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Author Chen, Shaokang
Berglund, Erik
Bigdeli, Abbas
Sanderson, Conrad
Lovell, Brian C.
Title of paper Experimental analysis of face recognition on still and CCTV images
Conference name IEEE International Conference on Advanced Video and Signal Based Surveillance 2008 (AVSS '08)
Conference location Santa Fe, New Mexico, U.S.A.
Conference dates 1-3 September 2008
Proceedings title Advanced Video and Signal Based Surveillance 2008
Place of Publication Piscataway, NJ, United States
Publisher IEEE - Institute of Electrical Electronics Engineers Inc.
Publication Year 2008
Sub-type Fully published paper
DOI 10.1109/AVSS.2008.15
ISBN 9780769533414
Editor I. Paulidis
I. Kakadiaris
Start page 317
End page 324
Total pages 8
Collection year 2009
Language eng
Abstract/Summary Although automatic identity inference based on faces has shown success when using high quality images, for CCTV based images it is hard to attain similar levels of performance. Furthermore, compared to recognition based on static images, relatively few studies have been done for video based face recognition. In this paper, we present an empirical analysis and comparison of face recognition using high quality and CCTV images in several important aspects: image quality (including resolution, noise, blurring and interlacing) as well as geometric transformations (such as translations, rotations and scale changes). The results show that holistic face recognition can be tolerant to image quality degradation but can also be highly influenced by geometric transformations. In addition, we show that camera intrinsics have much influence - when using different cameras for collecting gallery and probe images the recognition rate is considerably reduced. We also show that the classification performance can be considerably improved by straightforward averaging of consecutive face images from a CCTV video sequence.
Subjects 810107 National Security
970108 Expanding Knowledge in the Information and Computing Sciences
280203 Image Processing
280207 Pattern Recognition
080104 Computer Vision
Keyword CCTV images
Face recognition
Image classification
Image sequences
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Thu, 16 Apr 2009, 15:38:38 EST by Ms Kimberley Nunes on behalf of School of Information Technol and Elec Engineering