PTZ camera assisted face acquisition, tracking & recognition

Face recognition systems typically have a rather short operating distance with standoff (distance between the camera and the subject) limited to 1~2 meters. When these systems are used to capture face images at a larger distance (5~10 m), the resulting images contain only a small number of pixels on the face region, resulting in a degradation in face recognition performance. To address this problem, we propose a camera system consisting of one PTZ camera and two static cameras to acquire high resolution face images up to a distance of 10 meters. We propose a novel camera calibration method based on the coaxial configuration between the static and PTZ cameras. We also use a linear prediction model and camera control to mitigate delays in image processing and mechanical camera motion. The proposed system has a larger standoff in face image acquisition and effectiveness in face recognition test. Experimental results on video data collected at a distance ranging from 5 to 10 meters of 20 different subjects as probe and 10,020 subjects as gallery shows 96.4% rank-1 identification accuracy of the proposed method compared to 0.1% rank-1 accuracy of the conventional camera system using a state-of-the-art matcher.

[1]  Yi Yao,et al.  Improving long range and high magnification face recognition: Database acquisition, evaluation, and enhancement , 2008, Comput. Vis. Image Underst..

[2]  Yajie Tian,et al.  Handbook of face recognition , 2003 .

[3]  Alice J. O'Toole,et al.  FRVT 2006 and ICE 2006 large-scale results , 2007 .

[4]  Takeo Kanade,et al.  High-zoom video hallucination by exploiting spatio-temporal regularities , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[5]  Rita Cucchiara,et al.  Detecting Moving Objects, Ghosts, and Shadows in Video Streams , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Stan Z. Li,et al.  Tracking and Recognition of Multiple Faces at Distances , 2007, ICB.

[7]  Seong-Whan Lee,et al.  Stepwise Reconstruction of High-Resolution Facial Image Based on Interpolated Morphable Face Model , 2005, AVBPA.

[8]  Zhengyou Zhang,et al.  Flexible camera calibration by viewing a plane from unknown orientations , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[9]  Alice J. O'Toole,et al.  FRVT 2006 and ICE 2006 Large-Scale Experimental Results , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Massimo Tistarelli,et al.  Handbook of Remote Biometrics: for Surveillance and Security , 2009 .

[11]  Rainer Stiefelhagen,et al.  Automatic Person Detection and Tracking using Fuzzy Controlled Active Cameras , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Sharath Pankanti,et al.  Face cataloger: multi-scale imaging for relating identity to location , 2003, Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance, 2003..

[13]  Anil K. Jain,et al.  Handbook of Face Recognition, 2nd Edition , 2011 .

[14]  Roger Y. Tsai,et al.  A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses , 1987, IEEE J. Robotics Autom..

[15]  Rama Chellappa,et al.  Handbook of Remote Biometrics , 2009, Advances in Pattern Recognition.

[16]  J. Elder,et al.  Towards Face Recognition at a Distance , 2006 .

[17]  Irfan Essa,et al.  Tracking Multiple People with Multiple Cameras , 1998 .

[18]  Carlo S. Regazzoni,et al.  Cooperative multisensor system for real-time face detection and tracking in uncontrolled conditions , 2005, IS&T/SPIE Electronic Imaging.

[19]  Karl Ricanek,et al.  MORPH: a longitudinal image database of normal adult age-progression , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).