Video surveillance of passengers with mug shots

The authority officer relies on facial mug-shots to spot suspects among crowds. Passing through a check point, the facial displays and printouts operate in low resolution fixed poses. Thus, a databases-cuing video is recommended for real-time surveillance with Aided-Target Recognition (AiTR) prompting the inspector taking a closer second look at a specific passenger. Taking advantage of commercial available Face Detection System on Chips (SOC) at 0.04sec, we develop a fast and smart algorithm to sort facial poses among passengers. We can increase the overlapping POFs (pixels on faces) in matching mug shots at arbitrary poses with sorted facial poses. Lemma: We define the long exposure as time average of facial poses and the short exposure as single facial pose in a frame of video in 30 Hz. The fiduciary triangle is defined among two eyes and nose-top. Theorem Self-Reference Matched Filtering (Szu et al. Opt Comm. 1980; JOSA, 1982) to Facial-Pose: If we replace the desirable output of Weiner filter as the long exposure, then the filter can select a short exposure as the normal view. Corollary: Given a short exposure as normal view, the fiduciary triangle can decide all poses from left-to-right and top-to-down.

[1]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[2]  H. Sebastian Seung,et al.  The Manifold Ways of Perception , 2000, Science.

[3]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[4]  David J. Kriegman,et al.  Video-based face recognition using probabilistic appearance manifolds , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[5]  Harold H. Szu,et al.  Self-reference spatiotemporal image-restoration technique , 1982 .

[6]  Harold H. Szu,et al.  Local instances of good seeing , 1980 .

[7]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[8]  Alex Pentland,et al.  Bayesian Modeling of Facial Similarity , 1998, NIPS.

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

[10]  Hwaling Harold Szu,et al.  MO imagery techniques using arrays of large aperture telescopes , 1980 .

[11]  Rama Chellappa,et al.  Discriminant Analysis for Recognition of Human Face Images (Invited Paper) , 1997, AVBPA.

[12]  Dmitry O. Gorodnichy,et al.  Video-based framework for face recognition in video , 2005, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05).

[13]  Ronald R. Coifman,et al.  Data Fusion and Multicue Data Matching by Diffusion Maps , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Rama Chellappa,et al.  Probabilistic recognition of human faces from video , 2002, Proceedings. International Conference on Image Processing.

[15]  Zhou Zhi Eye Location Based on Hybrid Projection Function , 2003 .