3D Spatio-Temporal face recognition using dynamic range model sequences

Research on 3D face recognition has been intensified in recent years. However, most research has focused on the 3D static data analysis. In this paper, we investigate the face recognition problem using dynamic 3D face model sequences. Based on our newly created 3D dynamic face database, we propose to use a spatio-temporal hidden Markov model (HMM) which incorporates 3D surface feature characterization to learn the spatial and temporal information of faces. The advantage of using 3D dynamic data for face recognition has been evaluated by comparing our approach to three conventional approaches: 2D video based temporal HMM model, conventional 2D-texture based approach (e.g., Gabor wavelet based approach), and static 3D-model-based approaches.

[1]  Béla Ágai,et al.  CONDENSED 1,3,5-TRIAZEPINES - V THE SYNTHESIS OF PYRAZOLO [1,5-a] [1,3,5]-BENZOTRIAZEPINES , 1983 .

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

[3]  Ioannis A. Kakadiaris,et al.  Elastically Adaptive Deformable Models , 1996, ECCV.

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

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

[6]  Timothy F. Cootes,et al.  Improving identification performance by integrating evidence from sequences , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[7]  Monson H. Hayes,et al.  A hidden markov model-based approach for face detection and recognition , 1999 .

[8]  Avinash C. Kak,et al.  PCA versus LDA , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

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

[10]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

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

[12]  Thomas Vetter,et al.  Face Recognition Based on Fitting a 3D Morphable Model , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  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..

[14]  Shaogang Gong,et al.  Constructing Facial Identity Surfaces for Recognition , 2003, International Journal of Computer Vision.

[15]  Ahmed M. Elgammal,et al.  High Resolution Acquisition, Learning and Transfer of Dynamic 3‐D Facial Expressions , 2004, Comput. Graph. Forum.

[16]  Daniel Rueckert,et al.  Evaluation of automatic 4D face recognition using surface and texture registration , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[17]  Dimitris N. Metaxas,et al.  3D facial tracking from corrupted movie sequences , 2004, CVPR 2004.

[18]  Dimitris N. Metaxas,et al.  3D facial tracking from corrupted movie sequences , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[19]  Dimitris N. Metaxas,et al.  Optical Flow Constraints on Deformable Models with Applications to Face Tracking , 2000, International Journal of Computer Vision.

[20]  Patrick J. Flynn,et al.  Overview of the face recognition grand challenge , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[21]  Luiz Velho,et al.  Automatic 3D Facial Expression Analysis in Videos , 2005, AMFG.

[22]  Hiroshi Yasaka,et al.  1.3-V/sub pp/ push-pull drive InP Mach-Zehnder modulator module for 40 Gbit/s operation , 2005 .

[23]  Patrick J. Flynn,et al.  An evaluation of multimodal 2D+3D face biometrics , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Alexander M. Bronstein,et al.  Three-Dimensional Face Recognition , 2005, International Journal of Computer Vision.

[25]  Josef Kittler,et al.  3D Assisted Face Recognition: A Survey of 3D Imaging, Modelling and Recognition Approachest , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[26]  Remco C. Veltkamp,et al.  A Survey of 3D Face Recognition Methods , 2005, AVBPA.

[27]  Jun Wang,et al.  3D Facial Expression Recognition Based on Primitive Surface Feature Distribution , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[28]  Patrick J. Flynn,et al.  Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Enrico Grosso,et al.  Person Authentication from Video of Faces: A Behavioral and Physiological Approach Using Pseudo Hierarchical Hidden Markov Models , 2006, ICB.

[30]  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..

[31]  Anuj Srivastava,et al.  Three-Dimensional Face Recognition Using Shapes of Facial Curves , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Anil K. Jain,et al.  Matching 2.5D face scans to 3D models , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Ioannis A. Kakadiaris,et al.  Intraclass Retrieval of Nonrigid 3D Objects: Application to Face Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Lijun Yin,et al.  A high-resolution 3D dynamic facial expression database , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.