Sparse Variation Dictionary Learning for Face Recognition with a Single Training Sample per Person

Face recognition (FR) with a single training sample per person (STSPP) is a very challenging problem due to the lack of information to predict the variations in the query sample. Sparse representation based classification has shown interesting results in robust FR, however, its performance will deteriorate much for FR with STSPP. To address this issue, in this paper we learn a sparse variation dictionary from a generic training set to improve the query sample representation by STSPP. Instead of learning from the generic training set independently w.r.t. the gallery set, the proposed sparse variation dictionary learning (SVDL) method is adaptive to the gallery set by jointly learning a projection to connect the generic training set with the gallery set. The learnt sparse variation dictionary can be easily integrated into the framework of sparse representation based classification so that various variations in face images, including illumination, expression, occlusion, pose, etc., can be better handled. Experiments on the large-scale CMU Multi-PIE, FRGC and LFW databases demonstrate the promising performance of SVDL on FR with STSPP.

[1]  Pascal Müller,et al.  Realistic speech animation based on observed 3-D face dynamics , 2005 .

[2]  Daoqiang Zhang,et al.  A new face recognition method based on SVD perturbation for single example image per person , 2005, Appl. Math. Comput..

[3]  Zhi-Hua Zhou,et al.  Making FLDA applicable to face recognition with one sample per person , 2004, Pattern Recognit..

[4]  D. Hatzinakos,et al.  Expression Subspace Projection for Face Recognition from Single Sample per Person , 2012 .

[5]  Dorin Comaniciu,et al.  Total variation models for variable lighting face recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[7]  Jie Wang,et al.  On solving the face recognition problem with one training sample per subject , 2006, Pattern Recognit..

[8]  Michael Elad,et al.  Double Sparsity: Learning Sparse Dictionaries for Sparse Signal Approximation , 2010, IEEE Transactions on Signal Processing.

[9]  Gang Wang,et al.  Discriminative multi-manifold analysis for face recognition from a single training sample per person , 2011, ICCV.

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

[11]  Stefanos Zafeiriou,et al.  Subspace Learning from Image Gradient Orientations , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Wen Gao,et al.  Extended Fisherface for face recognition from a single example image per person , 2002, 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353).

[13]  Jean Ponce,et al.  Task-Driven Dictionary Learning , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Michael Elad,et al.  Dictionaries for Sparse Representation Modeling , 2010, Proceedings of the IEEE.

[15]  Gang Wang,et al.  Discriminative multi-manifold analysis for face recognition from a single training sample per person , 2011, 2011 International Conference on Computer Vision.

[16]  Jun Guo,et al.  Extended SRC: Undersampled Face Recognition via Intraclass Variant Dictionary , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Wen Gao,et al.  Coupled Bias–Variance Tradeoff for Cross-Pose Face Recognition , 2012, IEEE Transactions on Image Processing.

[18]  Zhi-Hua Zhou,et al.  Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft k-NN ensemble , 2005, IEEE Transactions on Neural Networks.

[19]  Tal Hassner,et al.  Effective Unconstrained Face Recognition by Combining Multiple Descriptors and Learned Background Statistics , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[21]  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).

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

[23]  Erik G. Learned-Miller,et al.  Unsupervised Joint Alignment of Complex Images , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[24]  Dimitrios Hatzinakos,et al.  Projection into Expression Subspaces for Face Recognition from Single Sample per Person , 2013, IEEE Transactions on Affective Computing.

[25]  Ammad Ali,et al.  Face Recognition with Local Binary Patterns , 2012 .

[26]  Jian Sun,et al.  An associate-predict model for face recognition , 2011, CVPR 2011.

[27]  Zhi-Hua Zhou,et al.  Face recognition from a single image per person: A survey , 2006, Pattern Recognit..

[28]  Shree K. Nayar,et al.  Attribute and simile classifiers for face verification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[29]  Wotao Yin,et al.  Group sparse optimization by alternating direction method , 2013, Optics & Photonics - Optical Engineering + Applications.

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

[31]  Josef Kittler,et al.  Locally linear discriminant analysis for multimodally distributed classes for face recognition with a single model image , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[34]  Wen Gao,et al.  Adaptive generic learning for face recognition from a single sample per person , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.