Kernel Coupled Cross-Regression for Low-Resolution Face Recognition

Low resolution (LR) in face recognition (FR) surveillance applications will cause the problem of dimensional mismatch between LR image and its high-resolution (HR) template. In this paper, a novel method called kernel coupled cross-regression (KCCR) is proposed to deal with this problem. Instead of processing in the original observing space directly, KCCR projects LR and HR face images into a unified nonlinear embedding feature space using kernel coupled mappings and graph embedding. Spectral regression is further employed to improve the generalization performance and reduce the time complexity. Meanwhile, cross-regression is developed to fully utilize the HR embedding to increase the information of the LR space, thus to improve the recognition performance. Experiments on the FERET and CMU PIE face database show that KCCR outperforms the existing structure-based methods in terms of recognition rate as well as time complexity.

[1]  Stan Z. Li,et al.  Low-resolution face recognition via Simultaneous Discriminant Analysis , 2011, 2011 International Joint Conference on Biometrics (IJCB).

[2]  Shaogang Gong,et al.  Multi-modal tensor face for simultaneous super-resolution and recognition , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[3]  Hyeonjoon Moon,et al.  The FERET Evaluation Methodology for Face-Recognition Algorithms , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Pong C. Yuen,et al.  Very low resolution face recognition problem , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[5]  Matti Pietikäinen,et al.  Local frequency descriptor for low-resolution face recognition , 2011, Face and Gesture 2011.

[6]  Takeo Kanade,et al.  Limits on Super-Resolution and How to Break Them , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Hong Yan,et al.  Coupled Kernel Embedding for Low-Resolution Face Image Recognition , 2012, IEEE Transactions on Image Processing.

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

[9]  Pong C. Yuen,et al.  Learning the Relationship Between High and Low Resolution Images in Kernel Space for Face Super Resolution , 2010, 2010 20th International Conference on Pattern Recognition.

[10]  Hanqing Lu,et al.  Supervised kernel locality preserving projections for face recognition , 2005, Neurocomputing.

[11]  Svetha Venkatesh,et al.  Face Recognition Using Kernel Ridge Regression , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Jiawei Han,et al.  Efficient Kernel Discriminant Analysis via Spectral Regression , 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

[13]  G. Baudat,et al.  Generalized Discriminant Analysis Using a Kernel Approach , 2000, Neural Computation.

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

[15]  D. Dai,et al.  Low-Resolution Face Recognition via Color Information and Regularized Coupled Mappings , 2010, 2010 Chinese Conference on Pattern Recognition (CCPR).

[16]  Yong Man Ro,et al.  Color Face Recognition for Degraded Face Images , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  Pablo H. Hennings-Yeomans,et al.  Simultaneous super-resolution and feature extraction for recognition of low-resolution faces , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Patrick J. Flynn,et al.  Multidimensional Scaling for Matching Low-Resolution Face Images , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Shuicheng Yan,et al.  Graph Embedding and Extensions: A General Framework for Dimensionality Reduction , 2007 .

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

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

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

[23]  Shiguang Shan,et al.  Low-Resolution Face Recognition via Coupled Locality Preserving Mappings , 2010, IEEE Signal Processing Letters.

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

[25]  B.V.K. Vijaya Kumar,et al.  How Low Can You Go? Low Resolution Face Recognition Study Using Kernel Correlation Feature Analysis on the FRGCv2 dataset , 2006, 2006 Biometrics Symposium: Special Session on Research at the Biometric Consortium Conference.