Misalignment-Robust Face Recognition

Subspace learning techniques for face recognition have been widely studied in the past three decades. In this paper, we study the problem of general subspace-based face recognition under the scenarios with spatial misalignments and/or image occlusions. For a given subspace derived from training data in a supervised, unsupervised, or semi-supervised manner, the embedding of a new datum and its underlying spatial misalignment parameters are simultaneously inferred by solving a constrained ¿1 norm optimization problem, which minimizes the ¿1 error between the misalignment-amended image and the image reconstructed from the given subspace along with its principal complementary subspace. A byproduct of this formulation is the capability to detect the underlying image occlusions. Extensive experiments on spatial misalignment estimation, image occlusion detection, and face recognition with spatial misalignments and/or image occlusions all validate the effectiveness of our proposed general formulation for misalignment-robust face recognition.

[1]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[2]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[3]  Jian Yang,et al.  KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Jieping Ye,et al.  Null space versus orthogonal linear discriminant analysis , 2006, ICML '06.

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

[6]  Rama Chellappa,et al.  Discriminant analysis of principal components for face recognition , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[7]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[8]  Wen Gao,et al.  Review the strength of Gabor features for face recognition from the angle of its robustness to mis-alignment , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[9]  Timothy F. Cootes,et al.  Comparing Active Shape Models with Active Appearance Models , 1999, BMVC.

[10]  Stephen Lin,et al.  Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[13]  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).

[14]  Aleix M. Martínez,et al.  Recognizing Imprecisely Localized, Partially Occluded, and Expression Variant Faces from a Single Sample per Class , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Qiang Ji,et al.  Automatic Eye Detection and Its Validation , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[16]  James H. Elder,et al.  Probabilistic Linear Discriminant Analysis for Inferences About Identity , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[17]  Juyang Weng,et al.  Using Discriminant Eigenfeatures for Image Retrieval , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Jieping Ye,et al.  Two-Dimensional Linear Discriminant Analysis , 2004, NIPS.

[19]  Yuxiao Hu,et al.  Face recognition using Laplacianfaces , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  Fei-Fei Li,et al.  Variational Shift Invariant Probabilistic PCA for Face Recognition , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[21]  Xiaogang Wang,et al.  Dual-space linear discriminant analysis for face recognition , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[22]  I. Jolliffe.  Principal Component Analysis , 2002 .

[23]  Wen Gao,et al.  Curse of mis-alignment in face recognition: problem and a novel mis-alignment learning solution , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[24]  Xiaogang Wang,et al.  A unified framework for subspace face recognition , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[25]  Xiaogang Wang,et al.  Random Sampling for Subspace Face Recognition , 2006, International Journal of Computer Vision.

[26]  Ming-Hsuan Yang,et al.  Kernel Eigenfaces vs. Kernel Fisherfaces: Face recognition using kernel methods , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[27]  Hwann-Tzong Chen,et al.  Local discriminant embedding and its variants , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[28]  Pavel Pudil,et al.  Introduction to Statistical Pattern Recognition , 2006 .

[29]  Jiawei Han,et al.  Spectral Regression: A Unified Approach for Sparse Subspace Learning , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[30]  Timothy F. Cootes,et al.  Active Appearance Models , 1998, ECCV.