Coupled Discriminant Analysis for Heterogeneous Face Recognition

Coupled space learning is an effective framework for heterogeneous face recognition. In this paper, we propose a novel coupled discriminant analysis method to improve the heterogeneous face recognition performance. There are two main advantages of the proposed method. First, all samples from different modalities are used to represent the coupled projections, so that sufficient discriminative information could be extracted. Second, the locality information in kernel space is incorporated into the coupled discriminant analysis as a constraint to improve the generalization ability. In particular, two implementations of locality constraint in kernel space (LCKS)-based coupled discriminant analysis methods, namely LCKS-coupled discriminant analysis (LCKS-CDA) and LCKS-coupled spectral regression (LCKS-CSR), are presented. Extensive experiments on three cases of heterogeneous face matching (high versus low image resolution, digital photo versus video image, and visible light versus near infrared) validate the efficacy of the proposed method.

[1]  Amit R.Sharma,et al.  Face Photo-Sketch Synthesis and Recognition , 2012 .

[2]  Gene H. Golub,et al.  Matrix Computations, Third Edition , 1996 .

[3]  Jiawei Han,et al.  Spectral Regression for Efficient Regularized Subspace Learning , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[4]  John Shawe-Taylor,et al.  Two view learning: SVM-2K, Theory and Practice , 2005, NIPS.

[5]  Stan Z. Li,et al.  Feature space locality constraint for kernel based nonlinear discriminant analysis , 2012, Pattern Recognit..

[6]  Stan Z. Li,et al.  Face Recognition with Local Gabor Textons , 2007, ICB.

[7]  Stan Z. Li,et al.  The HFB Face Database for Heterogeneous Face Biometrics research , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[8]  Shengcai Liao,et al.  Heterogeneous Face Recognition from Local Structures of Normalized Appearance , 2009, ICB.

[9]  Chengjun Liu,et al.  Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition , 2002, IEEE Trans. Image Process..

[10]  Yongdong Zhang,et al.  Multiview Spectral Embedding , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  Xiaogang Wang,et al.  Coupled information-theoretic encoding for face photo-sketch recognition , 2011, CVPR 2011.

[12]  Robert H. Halstead,et al.  Matrix Computations , 2011, Encyclopedia of Parallel Computing.

[13]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

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

[15]  Xuelong Li,et al.  General Tensor Discriminant Analysis and Gabor Features for Gait Recognition , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Stan Z. Li,et al.  Coupled Spectral Regression for matching heterogeneous faces , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[17]  Gene H. Golub,et al.  Matrix computations (3rd ed.) , 1996 .

[18]  Dahua Lin,et al.  Inter-modality Face Recognition , 2006, ECCV.

[19]  Dong Yi,et al.  Face Matching Between Near Infrared and Visible Light Images , 2007, ICB.

[20]  Xiaogang Wang,et al.  Face sketch recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[21]  Wen Gao,et al.  Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[22]  Hanqing Lu,et al.  A nonlinear approach for face sketch synthesis and recognition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[23]  Xuelong Li,et al.  Geometric Mean for Subspace Selection , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[27]  Stan Z. Li,et al.  2D–3D face matching using CCA , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[28]  V. Kshirsagar,et al.  Face recognition using Eigenfaces , 2011, 2011 3rd International Conference on Computer Research and Development.

[29]  Anil K. Jain,et al.  An improved coupled spectral regression for heterogeneous face recognition , 2012, 2012 5th IAPR International Conference on Biometrics (ICB).

[30]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression Database , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[31]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[32]  Cordelia Schmid,et al.  Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[33]  Matti Pietikäinen,et al.  Face Recognition by Exploring Information Jointly in Space, Scale and Orientation , 2011, IEEE Transactions on Image Processing.

[34]  Anil K. Jain,et al.  Heterogeneous Face Recognition: Matching NIR to Visible Light Images , 2010, 2010 20th International Conference on Pattern Recognition.

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

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