On intelligent surveillance systems and face recognition for mass transport security

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.

[1]  Horst Bischof,et al.  Robust recognition of scaled eigenimages through a hierarchical approach , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[2]  Chengjun Liu,et al.  Enhanced Fisher linear discriminant models for face recognition , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[3]  B. V. K. Vijaya Kumar,et al.  Face authentication for multiple subjects using eigenflow , 2003, Pattern Recognit..

[4]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, CAIP.

[6]  Aleix M. Martínez,et al.  Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Shimon Ullman,et al.  Face Recognition: The Problem of Compensating for Changes in Illumination Direction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  B. Lovell,et al.  Illumination and expression invariant face recognition with one sample image , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[9]  Brian C. Lovell,et al.  Towards Pose-Invariant 2D Face Classification for Surveillance , 2007, AMFG.

[10]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Chengjun Liu,et al.  Evolutionary Pursuit and Its Application to Face Recognition , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Yongsheng Gao,et al.  Face Recognition Using Line Edge Map , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  J. Kiefer,et al.  Sequential minimax search for a maximum , 1953 .

[14]  Vijayan K. Asari,et al.  A multi-view approach on modular PCA for illumination and pose invariant face recognition , 2004, 33rd Applied Imagery Pattern Recognition Workshop (AIPR'04).

[15]  P. Jonathon Phillips,et al.  Face recognition vendor test 2002 , 2003, 2003 IEEE International SOI Conference. Proceedings (Cat. No.03CH37443).

[16]  Brian C. Lovell,et al.  Vision Processing in Intelligent CCTV for Mass Transport Security , 2007 .

[17]  Samy Bengio,et al.  On transforming statistical models for non-frontal face verification , 2006, Pattern Recognit..

[18]  Ralph Gross,et al.  Appearance-based face recognition and light-fields , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Hans-Peter Seidel,et al.  Fitting a Morphable Model to 3D Scans of Faces , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[20]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  Ronen Basri,et al.  Lambertian Reflectance and Linear Subspaces , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Patrick J. Flynn,et al.  A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition , 2006, Comput. Vis. Image Underst..

[23]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Muhittin Gökmen,et al.  Eigenhill vs. eigenface and eigenedge , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[25]  David J. Fleet,et al.  Robustly Estimating Changes in Image Appearance , 2000, Comput. Vis. Image Underst..

[26]  Brian C. Lovell,et al.  Face Recognition Robust to Head Pose from One Sample Image , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[27]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[28]  Mohammed Bennamoun,et al.  An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Carlos D. Castillo,et al.  Using Stereo Matching for 2-D Face Recognition Across Pose , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[30]  Wen Gao,et al.  Virtual face image generation for illumination and pose insensitive face recognition , 2003, 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)..