Video-Based Face Recognition Algorithms

Traditional face recognition systems have relied on a gallery of still images for learning and a probe of still images for recognition. While the advantage of using motion information in face videos has been widely recognized, computational models for video-based face recognition have only recently gained attention. This chapter reviews some recent advances in this novel framework. In particular, the utility of videos in enhancing performance of image-based tasks (such as recognition or localization) will be summarized. Subsequently, spatiotemporal video-based face recognition systems based on particle filters, hidden Markov models , and system theoretic approaches will be presented. Further, some useful face databases employable by researchers interested in this field will be described. Finally, some open research issues will be proposed and discussed.

[1]  B. Anderson,et al.  Optimal Filtering , 1979, IEEE Transactions on Systems, Man, and Cybernetics.

[2]  Stefano Soatto,et al.  Dynamic Textures , 2003, International Journal of Computer Vision.

[3]  R Chellappa,et al.  Face verification through tracking facial features. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[4]  R. Kashyap.  A Bayesian comparison of different classes of dynamic models using empirical data , 1977 .

[5]  Manuele Bicego,et al.  Using hidden Markov models and wavelets for face recognition , 2003, 12th International Conference on Image Analysis and Processing, 2003.Proceedings..

[6]  Luc Vandendorpe,et al.  Face Authentication Competition on the BANCA Database , 2004, ICBA.

[7]  Alexander J. Smola,et al.  Binet-Cauchy Kernels on Dynamical Systems and its Application to the Analysis of Dynamic Scenes , 2007, International Journal of Computer Vision.

[8]  Nuno Vasconcelos,et al.  Probabilistic kernels for the classification of auto-regressive visual processes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[9]  Arun Ross,et al.  Handbook of Multibiometrics , 2006, The Kluwer international series on biometrics.

[10]  Enrico Grosso,et al.  Dynamic face recognition: From human to machine vision , 2009, Image Vis. Comput..

[11]  Tsuhan Chen,et al.  Video-based face recognition using adaptive hidden Markov models , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

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

[13]  Sargur N. Srihari,et al.  Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Alex Pentland,et al.  Parametrized structure from motion for 3D adaptive feedback tracking of faces , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  A. O'Toole,et al.  Recognizing moving faces: a psychological and neural synthesis , 2002, Trends in Cognitive Sciences.

[16]  Marco La Cascia,et al.  Fast, Reliable Head Tracking under Varying Illumination: An Approach Based on Registration of Texture-Mapped 3D Models , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Bart De Moor,et al.  Subspace angles between ARMA models , 2002, Syst. Control. Lett..

[18]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[19]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Uday B. Desai,et al.  Face recognition using a DCT-HMM approach , 1998, Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201).

[21]  Gregory D. Hager,et al.  Efficient Region Tracking With Parametric Models of Geometry and Illumination , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.

[23]  BicegoManuele,et al.  Dynamic face recognition , 2009 .

[24]  Rama Chellappa,et al.  3D Facial Pose Tracking in Uncalibrated Videos , 2005, PReMI.

[25]  Rama Chellappa,et al.  Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Rama Chellappa,et al.  A system identification approach for video-based face recognition , 2004, ICPR 2004.

[27]  N. Gordon,et al.  Novel approach to nonlinear/non-Gaussian Bayesian state estimation , 1993 .

[28]  David J. Kriegman,et al.  Visual tracking and recognition using probabilistic appearance manifolds , 2005, Comput. Vis. Image Underst..

[29]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[30]  Jean-Philippe Thiran,et al.  The BANCA Database and Evaluation Protocol , 2003, AVBPA.

[31]  Roberto Cipolla,et al.  Face Recognition from Video Using the Generic Shape-Illumination Manifold , 2006, ECCV.

[32]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[33]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[34]  Matti Pietikäinen,et al.  Electronic Letters on Computer Vision and Image Analysis 5(1):1-13, 2005 An Experimental Investigation about the Integration of Facial Dynamics in Video-Based Face Recognition , 2004 .

[35]  Alice J. O'Toole,et al.  A video database of moving faces and people , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[36]  Bart De Moor,et al.  N4SID: Subspace algorithms for the identification of combined deterministic-stochastic systems , 1994, Autom..

[37]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[38]  Luc Vandendorpe,et al.  Face authentication test on the BANCA database , 2004, ICPR 2004.

[39]  Rama Chellappa,et al.  Visual tracking and recognition using appearance-adaptive models in particle filters , 2004, IEEE Transactions on Image Processing.

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

[41]  Richard J. Martin.  A metric for ARMA processes , 2000, IEEE Trans. Signal Process..

[42]  Hervé Abdi,et al.  Learning the Moves: The Effect of Familiarity and Facial Motion on Person Recognition across Large Changes in Viewing Format , 2006, Perception.

[43]  Padhraic Smyth,et al.  Clustering Sequences with Hidden Markov Models , 1996, NIPS.

[44]  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..