Pose-robust face recognition via sparse representation

We propose a pose-robust face recognition method to handle the challenging task of face recognition in the presence of large pose difference between gallery and probe faces. The proposed method exploits the sparse property of the representation coefficients of a face image over its corresponding view-dictionary. By assuming the representation coefficients are invariant to pose, we can synthesize for the probe image a novel face image which has smaller pose difference with the gallery faces. Furthermore, face recognition in the presence of pose variations is achieved based on the synthesized face image again via sparse representation. Extensive experiments on CMU Multi-PIE face database are conducted to verify the efficacy of the proposed method.

[1]  Mohan M. Trivedi,et al.  Head Pose Estimation and Augmented Reality Tracking: An Integrated System and Evaluation for Monitoring Driver Awareness , 2010, IEEE Transactions on Intelligent Transportation Systems.

[2]  Osamu Yamaguchi,et al.  Face Recognition Using Multi-viewpoint Patterns for Robot Vision , 2003, ISRR.

[3]  David J. Kriegman,et al.  Clustering appearances of objects under varying illumination conditions , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

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

[5]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Takeo Kanade,et al.  Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[7]  Jonathan Warrell,et al.  Tied Factor Analysis for Face Recognition across Large Pose Differences , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  D. Jacobs,et al.  Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch , 2011, CVPR 2011.

[9]  Thomas S. Huang,et al.  Simultaneous discriminative projection and dictionary learning for sparse representation based classification , 2013, Pattern Recognit..

[10]  Wen Gao,et al.  Local Linear Regression (LLR) for Pose Invariant Face Recognition , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[11]  Gu-Min Jeong,et al.  Pixel selection based on discriminant features with application to face recognition , 2012, Pattern Recognit. Lett..

[12]  Dao-Qing Dai,et al.  Incremental learning of bidirectional principal components for face recognition , 2010, Pattern Recognit..

[13]  Hakan Cevikalp,et al.  Face recognition based on image sets , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[15]  J KriegmanDavid,et al.  Eigenfaces vs. Fisherfaces , 1997 .

[16]  Rama Chellappa,et al.  SFS based view synthesis for robust face recognition , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[17]  Thomas S. Huang,et al.  Multi-metric learning for multi-sensor fusion based classification , 2013, Inf. Fusion.

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

[19]  Rabab Kreidieh Ward,et al.  Component-wise pose normalization for pose-invariant face recognition , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[20]  Wen Gao,et al.  Locally Linear Regression for Pose-Invariant Face Recognition , 2007, IEEE Transactions on Image Processing.

[21]  Guillermo Sapiro,et al.  Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.

[22]  Thomas S. Huang,et al.  Close the loop: Joint blind image restoration and recognition with sparse representation prior , 2011, 2011 International Conference on Computer Vision.

[23]  Thomas S. Huang,et al.  Joint dynamic sparse representation for multi-view face recognition , 2012, Pattern Recognit..

[24]  Michael Elad,et al.  On the Role of Sparse and Redundant Representations in Image Processing , 2010, Proceedings of the IEEE.

[25]  Thomas S. Huang,et al.  Multi-View Automatic Target Recognition using Joint Sparse Representation , 2012, IEEE Transactions on Aerospace and Electronic Systems.

[26]  Daniel D. Lee,et al.  Grassmann discriminant analysis: a unifying view on subspace-based learning , 2008, ICML '08.

[27]  David Beymer,et al.  Face recognition under varying pose , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[28]  Habib Hamam,et al.  Double fusion filtering based multi-view face recognition , 2009 .

[29]  Tomaso A. Poggio,et al.  Linear Object Classes and Image Synthesis From a Single Example Image , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[30]  Jie Chen,et al.  Theoretical Results on Sparse Representations of Multiple-Measurement Vectors , 2006, IEEE Transactions on Signal Processing.

[31]  Murphy-ChutorianErik,et al.  Head pose estimation and augmented reality tracking , 2010 .

[32]  Vinod Kumar,et al.  Pose invariant virtual classifiers from single training image using novel hybrid-eigenfaces , 2010, Neurocomputing.

[33]  Rajat Raina,et al.  Efficient sparse coding algorithms , 2006, NIPS.

[34]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[35]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[36]  Thomas S. Huang,et al.  Joint-Structured-Sparsity-Based Classification for Multiple-Measurement Transient Acoustic Signals , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[37]  Michal Kawulok,et al.  Supervised relevance maps for increasing the distinctiveness of facial images , 2011, Pattern Recognit..

[38]  Pascal Frossard,et al.  Graph-based classification of multiple observation sets , 2010, Pattern Recognit..

[39]  Thomas Vetter,et al.  A morphable model for the synthesis of 3D faces , 1999, SIGGRAPH.

[40]  Thomas S. Huang,et al.  Heterogeneous multi-metric learning for multi-sensor fusion , 2011, 14th International Conference on Information Fusion.

[41]  Yihong Gong,et al.  Linear spatial pyramid matching using sparse coding for image classification , 2009, CVPR.

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

[43]  J. CandesE.,et al.  Robust uncertainty principles , 2006 .