Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2011, Article ID 745487, 17 pages doi: Research Article Co-Occurrence of Local Binary Patterns Features for Frontal Face Detection in Surveillance Applications Wael Louis and K. N. Plataniotis Multimedia Laboratory, The Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, 10 King’s College Road, Toronto, Canada M5S 3G4 Correspondence should be addressed to Wael Louis, wlouis@ Received 4 May 2010; Revised 16 September 2010; Accepted 9 December 2010 Academic Editor: Luigi Di Stefano Copyright © 2011 W. Louis and K. N. Plataniotis. This is an open access article distributed. | Hindawi Publishing Corporation EURASIP Journal on Image and Video Processing Volume 2011 Article ID 745487 17 pages doi 2011 745487 Research Article Co-Occurrence of Local Binary Patterns Features for Frontal Face Detection in Surveillance Applications Wael Louis and K. N. Plataniotis Multimedia Laboratory The Edward S. Rogers Department of Electrical and Computer Engineering University of Toronto 10 King s College Road Toronto Canada M5S3G4 Correspondence should be addressed to Wael Louis wlouis@ Received 4 May 2010 Revised 16 September 2010 Accepted 9 December 2010 Academic Editor Luigi Di Stefano Copyright 2011 W. Louis and K. N. Plataniotis. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. Face detection in video sequence is becoming popular in surveillance applications. The tradeoff between obtaining discriminative features to achieve accurate detection versus computational overhead of extracting these features which affects the classification speed is a persistent problem. This paper proposes to use multiple instances of rotational Local Binary Patterns LBP of pixels as features instead of using the histogram bins of the LBP of pixels. The multiple features are selected using the sequential forward selection algorithm we called Co-occurrence of LBP CoLBP . CoLBP feature extraction is computationally efficient and produces a high-performance rate. CoLBP features are used to implement a frontal face detector applied on a 2D low-resolution surveillance sequence. Experiments show that the CoLBP face features outperform state-of-the-art Haar-like features and various other LBP features extensions. Also the CoLBP features can tolerate a wide range of illumination and blurring changes. 1. Introduction Recently surveillance cameras and Closed-Circuit Television CCTV are available in .
