Joint dimensionality reduction for face recognition based on D-KSVD

Face recognition based on sparse representation is investigated in this paper. Dimensionality reduction is the process of projecting original image data into a low dimensional space, and usually be conducted before dictionary learning. However dimensionality reduction may lose information that is important to the face recognition task. Although the accuracy rate of face recognition varies with different projection matrixs, most of the existing methods ignore the importance of dimensionality reduction. To exploit more information from raw data, a new algorithm that combine the dimensionality reduction and dictionary learning based on discriminative K-SVD(D-KSVD) is proposed. The algorithm conducts dimensionality reduction and D-KSVD jointly for the purpose of learning a more powerful dictionary that has less reconstruction error. Experiments on several databases including AR, Extended YaleB and CMU PIE show the effectiveness of the proposed algorithm.

[1]  David Zhang,et al.  Collaborative Representation based Classification for Face Recognition , 2012, ArXiv.

[2]  Yang Gao,et al.  Bilinear discriminative dictionary learning for face recognition , 2014, Pattern Recognit..

[3]  David Zhang,et al.  Fisher Discrimination Dictionary Learning for sparse representation , 2011, 2011 International Conference on Computer Vision.

[4]  Joel A. Tropp,et al.  Greed is good: algorithmic results for sparse approximation , 2004, IEEE Transactions on Information Theory.

[5]  Yan Liu,et al.  Joint discriminative dimensionality reduction and dictionary learning for face recognition , 2013, Pattern Recognit..

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

[7]  Fan Zhang,et al.  Performance and theoretical study on corrosion inhibition of 2-(4-pyridyl)-benzimidazole for mild steel in hydrochloric acid , 2012 .

[8]  Yury Petrov,et al.  Harmony: EEG/MEG Linear Inverse Source Reconstruction in the Anatomical Basis of Spherical Harmonics , 2012, PloS one.

[9]  Aleix M. Martinez,et al.  The AR face database , 1998 .

[10]  Wotao Yin,et al.  A feasible method for optimization with orthogonality constraints , 2013, Math. Program..

[11]  David J. Kriegman,et al.  From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Yue Zhao,et al.  New Sparse Facial Feature Description Model Based on Salience Evaluation of Regions and Features , 2015, Int. J. Pattern Recognit. Artif. Intell..

[13]  A. Bruckstein,et al.  K-SVD : An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation , 2005 .

[14]  M. Elad,et al.  $rm K$-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation , 2006, IEEE Transactions on Signal Processing.

[15]  Larry S. Davis,et al.  Learning a discriminative dictionary for sparse coding via label consistent K-SVD , 2011, CVPR 2011.

[16]  A. Martínez,et al.  The AR face databasae , 1998 .

[17]  Ke Huang,et al.  Sparse Representation for Signal Classification , 2006, NIPS.

[18]  Jing-Yu Yang,et al.  Parity symmetrical collaborative representation-based classification for face recognition , 2017, Int. J. Mach. Learn. Cybern..

[19]  Yue Zhao,et al.  Sparse learning for salient facial feature description , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[20]  Joseph F. Murray,et al.  Dictionary Learning Algorithms for Sparse Representation , 2003, Neural Computation.

[21]  Baoxin Li,et al.  Discriminative K-SVD for dictionary learning in face recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Svetha Venkatesh,et al.  Joint learning and dictionary construction for pattern recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Lei Zhang,et al.  Sparse representation or collaborative representation: Which helps face recognition? , 2011, 2011 International Conference on Computer Vision.

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

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

[26]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[27]  Lei Zhang,et al.  Metaface learning for sparse representation based face recognition , 2010, 2010 IEEE International Conference on Image Processing.

[28]  Larry S. Davis,et al.  Label Consistent K-SVD: Learning a Discriminative Dictionary for Recognition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.