Recognising Moving Faces

An approach to engineering real-time computer vision systems for face recognition is described. The tasks considered involve recognition of multiple people in poorly constrained dynamic scenes. Modules for focus of attention, face detection, tracking and recognition are described. The need for integration of different processes using prediction and feedback is emphasised. Some examples from working systems are given.

[1]  B. Buxton,et al.  Monocular depth perception from optical flow by space time signal processing , 1983, Proceedings of the Royal Society of London. Series B. Biological Sciences.

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

[3]  T. Bachmann.  Identification of spatially quantised tachistoscopic images of faces: How many pixels does it take to carry identity? , 1991 .

[4]  J. H. Duncan,et al.  On the Detection of Motion and the Computation of Optical Flow , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[5]  John Daugman,et al.  High Confidence Visual Recognition of Persons by a Test of Statistical Independence , 1993, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Alex Pentland,et al.  View-based and modular eigenspaces for face recognition , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Alex Waibel,et al.  Face locating and tracking for human-computer interaction , 1994, Proceedings of 1994 28th Asilomar Conference on Signals, Systems and Computers.

[8]  Takeo Kanade,et al.  Human Face Detection in Visual Scenes , 1995, NIPS.

[9]  Nicholas Costen,et al.  Automatic Face Recognition: What Representation? , 1996, ECCV.

[10]  Shaogang Gong,et al.  Face Tracking and Pose Representation , 1996, BMVC.

[11]  D. Swets.  Discriminant An ct n from Vie , 1996 .

[12]  Tomaso Poggio,et al.  Image Representations for Visual Learning , 1996, Science.

[13]  Shaogang Gong,et al.  Tracking faces , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[14]  Shaogang Gong,et al.  Face Recognition in Dynamic Scenes , 1997, BMVC.

[15]  Rolf P. Würtz,et al.  Object Recognition Robust Under Translations, Deformations, and Changes in Background , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Norbert Krüger,et al.  Face recognition by elastic bunch graph matching , 1997, Proceedings of International Conference on Image Processing.

[17]  Shaogang Gong,et al.  Non-intrusive Person Authentication for Access Control by Visual Tracking and Face Recognition , 1997, AVBPA.

[18]  Timothy F. Cootes,et al.  Automatic Interpretation and Coding of Face Images Using Flexible Models , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  Stefan Fischer,et al.  Shape Normalisaiton for Face Recognition , 1997, AVBPA.

[20]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[21]  David J. Kriegman,et al.  Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection , 1996, ECCV.

[22]  K. Etemad,et al.  Discriminant analysis for recognition of human face images , 1997 .

[23]  Tomaso A. Poggio,et al.  Example-Based Learning for View-Based Human Face Detection , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Shaogang Gong,et al.  Real-time face pose estimation , 1998, Real Time Imaging.

[25]  Shaogang Gong,et al.  Segmentation and Tracking Using Color Mixture Models , 1998, ACCV.

[26]  Shaogang Gong,et al.  Tracking colour objects using adaptive mixture models , 1999, Image Vis. Comput..