Abstract

In recent years, sparse representation and dictionary-learning-based methods have emerged as powerful tools for efficiently processing data in nontraditional ways. A particular area of promise for these theories is face recognition. In this paper, we review the role of sparse representation and dictionary learning for efficient face identification and verification. Recent face recognition algorithms from still images, videos, and ambiguously labeled imagery are reviewed. In particular, discriminative dictionary learning algorithms as well as methods based on weakly supervised learning and domain adaptation are summarized. Some of the compelling challenges and issues that confront research in face recognition using sparse representations and dictionary learning are outlined.

© 2014 Optical Society of America

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2014 (1)

S. Shekhar, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Joint sparse representation for robust multimodal biometrics recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 113–126 (2014).
[CrossRef]

2013 (3)

Z. Jiang, Z. Lin, and L. S. Davis, “Label consistent k-svd: learning a discriminative dictionary for recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 35, 2651–2664 (2013).
[CrossRef]

H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Design of nonlinear kernel dictionaries for object recognition,” IEEE Trans. Image Process. 22, 5123–5135 (2013).
[CrossRef]

Y.-C. Chen, C. S. Sastry, V. M. Patel, P. J. Phillips, and R. Chellappa, “In-plane rotation and scale invariant clustering using dictionaries,” IEEE Trans. Image Process. 22, 2166–2180 (2013).
[CrossRef]

2012 (4)

J. Mairal, F. Bach, and J. Ponce, “Task-driven dictionary learning,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 791–804 (2012).
[CrossRef]

L. Zhang, W.-D. Zhou, P.-C. Chang, J. Liu, Z. Yan, T. Wang, and F.-Z. Li, “Kernel sparse representation-based classifier,” IEEE Trans. Signal Process. 60, 1684–1695 (2012).
[CrossRef]

V. M. Patel, W. Tao, S. Biswas, P. J. Phillips, and R. Chellappa, “Dictionary-based face recognition under variable lighting and pose,” IEEE Trans. Inf. Forensics Secur. 7, 954–965 (2012).
[CrossRef]

M. Elad, “Sparse and redundant representation modeling-what next?” IEEE Signal Process. Lett. 19, 922–928 (2012).
[CrossRef]

2011 (5)

P. J. Phillips, “Improving face recognition technology,” Computer 44, 84–86 (2011).
[CrossRef]

A. J. O’Toole, J. Harms, S. L. Snow, D. R. Hurst, M. R. Pappas, J. H. Ayyad, and H. Abdi, “Recognizing people from dynamic and static faces and bodies: dissecting identity with a fusion approach,” Vis. Res. 51, 74–83 (2011).
[CrossRef]

P. K. Turaga, A. Veeraraghavan, A. Srivastava, and R. Chellappa, “Statistical computations on Grassmann and Stiefel manifolds for image and video-based recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 2273–2286 (2011).
[CrossRef]

E. J. Candès, X. Li, Y. Ma, and J. Wright, “Robust principal component analysis?” J. ACM 58(3), 1–37 (2011).
[CrossRef]

J. K. Pillai, V. M. Patel, R. Chellappa, and N. Ratha, “Secure and robust iris recognition using random projections and sparse representations,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 1877–1893 (2011).
[CrossRef]

2010 (4)

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
[CrossRef]

R. Rubinstein, A. M. Bruckstein, and M. Elad, “Dictionaries for sparse representation modeling,” Proc. IEEE 98, 1045–1057 (2010).
[CrossRef]

R. Gross, I. Matthews, J. F. Cohn, T. Kanade, and S. Baker, “Multi-PIE,” Image Vis. Comput. 28, 807–813 (2010).

B. Li, H. Chang, S. Shan, and X. Chen, “Low-resolution face recognition via coupled locality preserving mappings,” IEEE Signal Process. Lett. 17, 20–23 (2010).
[CrossRef]

2009 (2)

S. Biswas, G. Aggarwal, and R. Chellappa, “Robust estimation of albedo for illumination-invariant matching and shape recovery,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 884–899 (2009).
[CrossRef]

J. Wright, A. Y. Yang, A. A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[CrossRef]

2006 (1)

M. Aharon, M. Elad, and A. M. Bruckstein, “The k-svd: an algorithm for designing of overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).
[CrossRef]

2003 (2)

V. Blanz and T. Vetter, “Face recognition based on fitting a 3d morphable model,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1063–1074 (2003).
[CrossRef]

W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: a literature survey,” ACM Computing Surveys 35, 399–458 (2003).
[CrossRef]

2002 (1)

M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, “Face recognition by independent component analysis,” IEEE Trans. Neural Netw. 13, 1450–1464 (2002).
[CrossRef]

2001 (1)

A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From few to many: illumination cone models for face recognition under variable lighting and pose,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 643–660 (2001).
[CrossRef]

1998 (3)

K. Etemand and R. Chellappa, “Separability-based multiscale basis selection and feature extraction for signal and image classification,” IEEE Trans. Image Process. 7, 1453–1465 (1998).
[CrossRef]

S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comp 20, 33–61 (1998).
[CrossRef]

P. J. Phillips, “Matching pursuit filters applied to face identification,” IEEE Trans. Image Process. 7, 1150–1164 (1998).
[CrossRef]

1997 (2)

P. Belhumeur, J. Hespanda, and D. Kriegman, “Eigenfaces versus fisherfaces: recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997).
[CrossRef]

K. Etemad and R. Chellappa, “Discriminant analysis for recognition of human face images,” J. Opt. Soc. Am. A 14, 1724–1733 (1997).
[CrossRef]

1991 (1)

M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cogn. Neurosci. 3, 71–86 (1991).

Aase, S. O.

K. Engan, S. O. Aase, and J. H. Husoy, “Method of optimal directions for frame design,” in IEEE International Conference on Acoustic, Speech, Signal Processing, Phoenix, 1999, Vol. 5, pp. 2443–2446.

Abdi, H.

A. J. O’Toole, J. Harms, S. L. Snow, D. R. Hurst, M. R. Pappas, J. H. Ayyad, and H. Abdi, “Recognizing people from dynamic and static faces and bodies: dissecting identity with a fusion approach,” Vis. Res. 51, 74–83 (2011).
[CrossRef]

Aggarwal, G.

S. Biswas, G. Aggarwal, and R. Chellappa, “Robust estimation of albedo for illumination-invariant matching and shape recovery,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 884–899 (2009).
[CrossRef]

Aharon, M.

M. Aharon, M. Elad, and A. M. Bruckstein, “The k-svd: an algorithm for designing of overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).
[CrossRef]

Ambadar, Z.

P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, “The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops, San Francisco, 2010, pp. 94–101.

Ayyad, J. H.

A. J. O’Toole, J. Harms, S. L. Snow, D. R. Hurst, M. R. Pappas, J. H. Ayyad, and H. Abdi, “Recognizing people from dynamic and static faces and bodies: dissecting identity with a fusion approach,” Vis. Res. 51, 74–83 (2011).
[CrossRef]

Bach, F.

J. Mairal, F. Bach, and J. Ponce, “Task-driven dictionary learning,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 791–804 (2012).
[CrossRef]

Baker, S.

R. Gross, I. Matthews, J. F. Cohn, T. Kanade, and S. Baker, “Multi-PIE,” Image Vis. Comput. 28, 807–813 (2010).

Bartlett, M. S.

M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, “Face recognition by independent component analysis,” IEEE Trans. Neural Netw. 13, 1450–1464 (2002).
[CrossRef]

Belhumeur, P.

P. Belhumeur, J. Hespanda, and D. Kriegman, “Eigenfaces versus fisherfaces: recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997).
[CrossRef]

Belhumeur, P. N.

A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From few to many: illumination cone models for face recognition under variable lighting and pose,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 643–660 (2001).
[CrossRef]

Biswas, S.

V. M. Patel, W. Tao, S. Biswas, P. J. Phillips, and R. Chellappa, “Dictionary-based face recognition under variable lighting and pose,” IEEE Trans. Inf. Forensics Secur. 7, 954–965 (2012).
[CrossRef]

S. Biswas, G. Aggarwal, and R. Chellappa, “Robust estimation of albedo for illumination-invariant matching and shape recovery,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 884–899 (2009).
[CrossRef]

S. Biswas and R. Chellappa, “Pose-robust albedo estimation from a single image,” in IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010.

Blanz, V.

V. Blanz and T. Vetter, “Face recognition based on fitting a 3d morphable model,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1063–1074 (2003).
[CrossRef]

Bolme, D.

P. Jonathon Phillips, P. J. Flynn, J. Ross Beveridge, W. Todd Scruggs, A. J. O’Toole, D. Bolme, K. W. Bowyer, B. A. Draper, G. H. Givens, Y. M. Lui, H. Sahibzada, J. A. Scallan, and S. Weimer, “Overview of the multiple biometrics grand challenge,” in International Conference on Biometrics (Springer, 2009), pp. 705–714.

Bowyer, K. W.

P. Jonathon Phillips, P. J. Flynn, J. Ross Beveridge, W. Todd Scruggs, A. J. O’Toole, D. Bolme, K. W. Bowyer, B. A. Draper, G. H. Givens, Y. M. Lui, H. Sahibzada, J. A. Scallan, and S. Weimer, “Overview of the multiple biometrics grand challenge,” in International Conference on Biometrics (Springer, 2009), pp. 705–714.

P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, “Overview of the face recognition grand challenge,” in IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005, Vol. 1, pp. 947–954.

Bruckstein, A. M.

R. Rubinstein, A. M. Bruckstein, and M. Elad, “Dictionaries for sparse representation modeling,” Proc. IEEE 98, 1045–1057 (2010).
[CrossRef]

M. Aharon, M. Elad, and A. M. Bruckstein, “The k-svd: an algorithm for designing of overcomplete dictionaries for sparse representation,” IEEE Trans. Signal Process. 54, 4311–4322 (2006).
[CrossRef]

Candès, E. J.

E. J. Candès, X. Li, Y. Ma, and J. Wright, “Robust principal component analysis?” J. ACM 58(3), 1–37 (2011).
[CrossRef]

Chang, H.

B. Li, H. Chang, S. Shan, and X. Chen, “Low-resolution face recognition via coupled locality preserving mappings,” IEEE Signal Process. Lett. 17, 20–23 (2010).
[CrossRef]

Chang, J.

P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, “Overview of the face recognition grand challenge,” in IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005, Vol. 1, pp. 947–954.

Chang, P.-C.

L. Zhang, W.-D. Zhou, P.-C. Chang, J. Liu, Z. Yan, T. Wang, and F.-Z. Li, “Kernel sparse representation-based classifier,” IEEE Trans. Signal Process. 60, 1684–1695 (2012).
[CrossRef]

Chellappa, R.

S. Shekhar, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Joint sparse representation for robust multimodal biometrics recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 113–126 (2014).
[CrossRef]

H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Design of nonlinear kernel dictionaries for object recognition,” IEEE Trans. Image Process. 22, 5123–5135 (2013).
[CrossRef]

Y.-C. Chen, C. S. Sastry, V. M. Patel, P. J. Phillips, and R. Chellappa, “In-plane rotation and scale invariant clustering using dictionaries,” IEEE Trans. Image Process. 22, 2166–2180 (2013).
[CrossRef]

V. M. Patel, W. Tao, S. Biswas, P. J. Phillips, and R. Chellappa, “Dictionary-based face recognition under variable lighting and pose,” IEEE Trans. Inf. Forensics Secur. 7, 954–965 (2012).
[CrossRef]

J. K. Pillai, V. M. Patel, R. Chellappa, and N. Ratha, “Secure and robust iris recognition using random projections and sparse representations,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 1877–1893 (2011).
[CrossRef]

P. K. Turaga, A. Veeraraghavan, A. Srivastava, and R. Chellappa, “Statistical computations on Grassmann and Stiefel manifolds for image and video-based recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 2273–2286 (2011).
[CrossRef]

S. Biswas, G. Aggarwal, and R. Chellappa, “Robust estimation of albedo for illumination-invariant matching and shape recovery,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 884–899 (2009).
[CrossRef]

W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: a literature survey,” ACM Computing Surveys 35, 399–458 (2003).
[CrossRef]

K. Etemand and R. Chellappa, “Separability-based multiscale basis selection and feature extraction for signal and image classification,” IEEE Trans. Image Process. 7, 1453–1465 (1998).
[CrossRef]

K. Etemad and R. Chellappa, “Discriminant analysis for recognition of human face images,” J. Opt. Soc. Am. A 14, 1724–1733 (1997).
[CrossRef]

S. Shekhar, V. M. Patel, and R. Chellappa, “Synthesis-based recognition of low resolution faces,” in International Joint Conference on Biometrics, Washington, D.C., 2011, pp. 1–6.

A. Shrivastava, J. K. Pillai, V. M. Patel, and R. Chellappa, “Learning discriminative dictionaries with partially labeled data,” in IEEE International Conference on Image ProcessingOrlando, 2012, pp. 3113–3116.

Q. Qiu, V. M. Patel, P. Turaga, and R. Chellappa, “Domain adaptive dictionary learning,” in European Conference on Computer Vision (2012), Vol. 7575, pp. 631–645.

V. M. Patel, R. Chellappa, and M. Tistarelli, “Sparse representations and random projections for robust and cancelable biometrics,” in International Conference on Control, Automation, Robotics and Vision, Guangzhou, December, 2010, pp. 1–6.

Y.-C. Chen, V. M. Patel, R. Chellappa, and P. J. Phillips, “Salient views and view-dependent dictionaries for object recognition,” Comput. Vis. Image Underst. (to be published).

V. M. Patel and R. Chellappa, “Sparse representations, compressive sensing and dictionaries for pattern recognition,” in Asian Conference on Pattern Recognition (ACPR), Beijing, 2010.

J. Ni, Q. Qiu, and R. Chellappa, “Subspace interpolation via dictionary learning for unsupervised domain adaptation,” in Computer Vision and Pattern Recognition (CVPR), Washington, D.C., 2013, pp. 692–699.

S. Biswas and R. Chellappa, “Pose-robust albedo estimation from a single image,” in IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010.

S. Taheri, V. M. Patel, and R. Chellappa, “Component-based recognition of faces and facial expressions,” in IEEE Transactions on Affective Computing, October, 2013.

Y.-C. Chen, V. M. Patel, S. Shekhar, R. Chellappa, and P. J. Phillips, “Video-based face recognition via joint sparse representation,” in IEEE International Conference on Automatic Face and Gesture Recognition, Shanghai, 2013, pp. 1–8.

Y.-C. Chen, V. M. Patel, P. J. Phillips, and R. Chellappa, “Dictionary-based face recognition from video,” in European Conference on Computer Vision, October 2012.

A. Shrivastava, H. V. Nguyen, V. M. Patel, and R. Chellappa, “Design of nonlinear discriminative dictionaries for image classification,” in Asian Conference on Computer Vision (ACCV) (Springer-Verlag, 2013), pp. 660–674.

Y.-C. Chen, V. M. Patel, J. K. Pillai, R. Chellappa, and P. J. Phillips, “Dictionary learning from ambiguously labeled data,” in IEEE Conference on Computer Vision and Pattern Recognition, Portland, Oregon, 2013, pp. 353–360.

Q. Qiu, V. M. Patel, and R. Chellappa, “Information-theoretic dictionary learning for image classification,” IEEE Trans. Pattern Anal. Mach. Intell. (to be published).

S. Shekhar, V. M. Patel, H. V. Nguyen, and R. Chellappa, “Generalized domain-adaptive dictionaries,” in Conference on Computer Vision and Pattern Recognition (IEEE, 2013), pp. 361–368.

P. K. Turaga, A. Veeraraghavan, and R. Chellappa, “Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision,” in IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, 2008, pp. 1–8.

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S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comp 20, 33–61 (1998).
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B. Li, H. Chang, S. Shan, and X. Chen, “Low-resolution face recognition via coupled locality preserving mappings,” IEEE Signal Process. Lett. 17, 20–23 (2010).
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Y.-C. Chen, C. S. Sastry, V. M. Patel, P. J. Phillips, and R. Chellappa, “In-plane rotation and scale invariant clustering using dictionaries,” IEEE Trans. Image Process. 22, 2166–2180 (2013).
[CrossRef]

Y.-C. Chen, V. M. Patel, R. Chellappa, and P. J. Phillips, “Salient views and view-dependent dictionaries for object recognition,” Comput. Vis. Image Underst. (to be published).

Y.-C. Chen, V. M. Patel, J. K. Pillai, R. Chellappa, and P. J. Phillips, “Dictionary learning from ambiguously labeled data,” in IEEE Conference on Computer Vision and Pattern Recognition, Portland, Oregon, 2013, pp. 353–360.

Y.-C. Chen, V. M. Patel, P. J. Phillips, and R. Chellappa, “Dictionary-based face recognition from video,” in European Conference on Computer Vision, October 2012.

Y.-C. Chen, V. M. Patel, S. Shekhar, R. Chellappa, and P. J. Phillips, “Video-based face recognition via joint sparse representation,” in IEEE International Conference on Automatic Face and Gesture Recognition, Shanghai, 2013, pp. 1–8.

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R. Gross, I. Matthews, J. F. Cohn, T. Kanade, and S. Baker, “Multi-PIE,” Image Vis. Comput. 28, 807–813 (2010).

P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, “The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops, San Francisco, 2010, pp. 94–101.

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K. Saenko, B. Kulis, M. Fritz, and T. Darrell, “Adapting visual category models to new domains,” in European Conference on Computer Vision (Springer-Verlag, 2010), Vol. 6314, pp. 213–226.

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S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comp 20, 33–61 (1998).
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P. Jonathon Phillips, P. J. Flynn, J. Ross Beveridge, W. Todd Scruggs, A. J. O’Toole, D. Bolme, K. W. Bowyer, B. A. Draper, G. H. Givens, Y. M. Lui, H. Sahibzada, J. A. Scallan, and S. Weimer, “Overview of the multiple biometrics grand challenge,” in International Conference on Biometrics (Springer, 2009), pp. 705–714.

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P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, “Overview of the face recognition grand challenge,” in IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005, Vol. 1, pp. 947–954.

P. Jonathon Phillips, P. J. Flynn, J. Ross Beveridge, W. Todd Scruggs, A. J. O’Toole, D. Bolme, K. W. Bowyer, B. A. Draper, G. H. Givens, Y. M. Lui, H. Sahibzada, J. A. Scallan, and S. Weimer, “Overview of the multiple biometrics grand challenge,” in International Conference on Biometrics (Springer, 2009), pp. 705–714.

Fritz, M.

K. Saenko, B. Kulis, M. Fritz, and T. Darrell, “Adapting visual category models to new domains,” in European Conference on Computer Vision (Springer-Verlag, 2010), Vol. 6314, pp. 213–226.

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J. Wright, A. Y. Yang, A. A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
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P. Jonathon Phillips, P. J. Flynn, J. Ross Beveridge, W. Todd Scruggs, A. J. O’Toole, D. Bolme, K. W. Bowyer, B. A. Draper, G. H. Givens, Y. M. Lui, H. Sahibzada, J. A. Scallan, and S. Weimer, “Overview of the multiple biometrics grand challenge,” in International Conference on Biometrics (Springer, 2009), pp. 705–714.

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R. Gross, I. Matthews, J. F. Cohn, T. Kanade, and S. Baker, “Multi-PIE,” Image Vis. Comput. 28, 807–813 (2010).

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A. J. O’Toole, J. Harms, S. L. Snow, D. R. Hurst, M. R. Pappas, J. H. Ayyad, and H. Abdi, “Recognizing people from dynamic and static faces and bodies: dissecting identity with a fusion approach,” Vis. Res. 51, 74–83 (2011).
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P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, “Overview of the face recognition grand challenge,” in IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005, Vol. 1, pp. 947–954.

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Y. Hu, A. S. Mian, and R. Owens, “Sparse approximated nearest points for image set classification,” in Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 27–40.

Huang, T.

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010, pp. 3360–3367.

Huang, T. S.

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
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A. J. O’Toole, J. Harms, S. L. Snow, D. R. Hurst, M. R. Pappas, J. H. Ayyad, and H. Abdi, “Recognizing people from dynamic and static faces and bodies: dissecting identity with a fusion approach,” Vis. Res. 51, 74–83 (2011).
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K. Engan, S. O. Aase, and J. H. Husoy, “Method of optimal directions for frame design,” in IEEE International Conference on Acoustic, Speech, Signal Processing, Phoenix, 1999, Vol. 5, pp. 2443–2446.

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J. Jiang, “A literature survey on domain adaptation of statistical classifiers,” (2008).

Jiang, Z.

Z. Jiang, Z. Lin, and L. S. Davis, “Label consistent k-svd: learning a discriminative dictionary for recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 35, 2651–2664 (2013).
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Jonathon Phillips, P.

P. Jonathon Phillips, P. J. Flynn, J. Ross Beveridge, W. Todd Scruggs, A. J. O’Toole, D. Bolme, K. W. Bowyer, B. A. Draper, G. H. Givens, Y. M. Lui, H. Sahibzada, J. A. Scallan, and S. Weimer, “Overview of the multiple biometrics grand challenge,” in International Conference on Biometrics (Springer, 2009), pp. 705–714.

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P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” In IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, June, 2001, pp. 511–518.

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R. Gross, I. Matthews, J. F. Cohn, T. Kanade, and S. Baker, “Multi-PIE,” Image Vis. Comput. 28, 807–813 (2010).

P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, “The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops, San Francisco, 2010, pp. 94–101.

Kriegman, D.

P. Belhumeur, J. Hespanda, and D. Kriegman, “Eigenfaces versus fisherfaces: recognition using class specific linear projection,” IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997).
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Kriegman, D. J.

A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, “From few to many: illumination cone models for face recognition under variable lighting and pose,” IEEE Trans. Pattern Anal. Mach. Intell. 23, 643–660 (2001).
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Kulis, B.

K. Saenko, B. Kulis, M. Fritz, and T. Darrell, “Adapting visual category models to new domains,” in European Conference on Computer Vision (Springer-Verlag, 2010), Vol. 6314, pp. 213–226.

Li, B.

B. Li, H. Chang, S. Shan, and X. Chen, “Low-resolution face recognition via coupled locality preserving mappings,” IEEE Signal Process. Lett. 17, 20–23 (2010).
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Q. Zhang and B. Li, “Discriminative k-svd for dictionary learning in face recognition,” in IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010.

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L. Zhang, W.-D. Zhou, P.-C. Chang, J. Liu, Z. Yan, T. Wang, and F.-Z. Li, “Kernel sparse representation-based classifier,” IEEE Trans. Signal Process. 60, 1684–1695 (2012).
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E. J. Candès, X. Li, Y. Ma, and J. Wright, “Robust principal component analysis?” J. ACM 58(3), 1–37 (2011).
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Z. Jiang, Z. Lin, and L. S. Davis, “Label consistent k-svd: learning a discriminative dictionary for recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 35, 2651–2664 (2013).
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L. Zhang, W.-D. Zhou, P.-C. Chang, J. Liu, Z. Yan, T. Wang, and F.-Z. Li, “Kernel sparse representation-based classifier,” IEEE Trans. Signal Process. 60, 1684–1695 (2012).
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P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, “The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops, San Francisco, 2010, pp. 94–101.

Lui, Y. M.

P. Jonathon Phillips, P. J. Flynn, J. Ross Beveridge, W. Todd Scruggs, A. J. O’Toole, D. Bolme, K. W. Bowyer, B. A. Draper, G. H. Givens, Y. M. Lui, H. Sahibzada, J. A. Scallan, and S. Weimer, “Overview of the multiple biometrics grand challenge,” in International Conference on Biometrics (Springer, 2009), pp. 705–714.

Lv, F.

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010, pp. 3360–3367.

Ma, Y.

E. J. Candès, X. Li, Y. Ma, and J. Wright, “Robust principal component analysis?” J. ACM 58(3), 1–37 (2011).
[CrossRef]

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
[CrossRef]

J. Wright, A. Y. Yang, A. A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
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J. Mairal, F. Bach, and J. Ponce, “Task-driven dictionary learning,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 791–804 (2012).
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J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
[CrossRef]

Marques, J.

P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, “Overview of the face recognition grand challenge,” in IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005, Vol. 1, pp. 947–954.

Matthews, I.

R. Gross, I. Matthews, J. F. Cohn, T. Kanade, and S. Baker, “Multi-PIE,” Image Vis. Comput. 28, 807–813 (2010).

P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, “The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops, San Francisco, 2010, pp. 94–101.

Mian, A. S.

Y. Hu, A. S. Mian, and R. Owens, “Sparse approximated nearest points for image set classification,” in Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 27–40.

Min, J.

P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, “Overview of the face recognition grand challenge,” in IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005, Vol. 1, pp. 947–954.

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S. Shekhar, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Joint sparse representation for robust multimodal biometrics recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 113–126 (2014).
[CrossRef]

H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Design of nonlinear kernel dictionaries for object recognition,” IEEE Trans. Image Process. 22, 5123–5135 (2013).
[CrossRef]

Nguyen, H. V.

H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Design of nonlinear kernel dictionaries for object recognition,” IEEE Trans. Image Process. 22, 5123–5135 (2013).
[CrossRef]

S. Shekhar, V. M. Patel, H. V. Nguyen, and R. Chellappa, “Generalized domain-adaptive dictionaries,” in Conference on Computer Vision and Pattern Recognition (IEEE, 2013), pp. 361–368.

A. Shrivastava, H. V. Nguyen, V. M. Patel, and R. Chellappa, “Design of nonlinear discriminative dictionaries for image classification,” in Asian Conference on Computer Vision (ACCV) (Springer-Verlag, 2013), pp. 660–674.

Ni, J.

J. Ni, Q. Qiu, and R. Chellappa, “Subspace interpolation via dictionary learning for unsupervised domain adaptation,” in Computer Vision and Pattern Recognition (CVPR), Washington, D.C., 2013, pp. 692–699.

O’Toole, A. J.

A. J. O’Toole, J. Harms, S. L. Snow, D. R. Hurst, M. R. Pappas, J. H. Ayyad, and H. Abdi, “Recognizing people from dynamic and static faces and bodies: dissecting identity with a fusion approach,” Vis. Res. 51, 74–83 (2011).
[CrossRef]

P. Jonathon Phillips, P. J. Flynn, J. Ross Beveridge, W. Todd Scruggs, A. J. O’Toole, D. Bolme, K. W. Bowyer, B. A. Draper, G. H. Givens, Y. M. Lui, H. Sahibzada, J. A. Scallan, and S. Weimer, “Overview of the multiple biometrics grand challenge,” in International Conference on Biometrics (Springer, 2009), pp. 705–714.

Owens, R.

Y. Hu, A. S. Mian, and R. Owens, “Sparse approximated nearest points for image set classification,” in Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 27–40.

Pappas, M. R.

A. J. O’Toole, J. Harms, S. L. Snow, D. R. Hurst, M. R. Pappas, J. H. Ayyad, and H. Abdi, “Recognizing people from dynamic and static faces and bodies: dissecting identity with a fusion approach,” Vis. Res. 51, 74–83 (2011).
[CrossRef]

Patel, V. M.

S. Shekhar, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Joint sparse representation for robust multimodal biometrics recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 113–126 (2014).
[CrossRef]

H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Design of nonlinear kernel dictionaries for object recognition,” IEEE Trans. Image Process. 22, 5123–5135 (2013).
[CrossRef]

Y.-C. Chen, C. S. Sastry, V. M. Patel, P. J. Phillips, and R. Chellappa, “In-plane rotation and scale invariant clustering using dictionaries,” IEEE Trans. Image Process. 22, 2166–2180 (2013).
[CrossRef]

V. M. Patel, W. Tao, S. Biswas, P. J. Phillips, and R. Chellappa, “Dictionary-based face recognition under variable lighting and pose,” IEEE Trans. Inf. Forensics Secur. 7, 954–965 (2012).
[CrossRef]

J. K. Pillai, V. M. Patel, R. Chellappa, and N. Ratha, “Secure and robust iris recognition using random projections and sparse representations,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 1877–1893 (2011).
[CrossRef]

S. Taheri, V. M. Patel, and R. Chellappa, “Component-based recognition of faces and facial expressions,” in IEEE Transactions on Affective Computing, October, 2013.

Y.-C. Chen, V. M. Patel, S. Shekhar, R. Chellappa, and P. J. Phillips, “Video-based face recognition via joint sparse representation,” in IEEE International Conference on Automatic Face and Gesture Recognition, Shanghai, 2013, pp. 1–8.

A. Shrivastava, H. V. Nguyen, V. M. Patel, and R. Chellappa, “Design of nonlinear discriminative dictionaries for image classification,” in Asian Conference on Computer Vision (ACCV) (Springer-Verlag, 2013), pp. 660–674.

Y.-C. Chen, V. M. Patel, P. J. Phillips, and R. Chellappa, “Dictionary-based face recognition from video,” in European Conference on Computer Vision, October 2012.

Q. Qiu, V. M. Patel, and R. Chellappa, “Information-theoretic dictionary learning for image classification,” IEEE Trans. Pattern Anal. Mach. Intell. (to be published).

S. Shekhar, V. M. Patel, H. V. Nguyen, and R. Chellappa, “Generalized domain-adaptive dictionaries,” in Conference on Computer Vision and Pattern Recognition (IEEE, 2013), pp. 361–368.

Y.-C. Chen, V. M. Patel, J. K. Pillai, R. Chellappa, and P. J. Phillips, “Dictionary learning from ambiguously labeled data,” in IEEE Conference on Computer Vision and Pattern Recognition, Portland, Oregon, 2013, pp. 353–360.

Y.-C. Chen, V. M. Patel, R. Chellappa, and P. J. Phillips, “Salient views and view-dependent dictionaries for object recognition,” Comput. Vis. Image Underst. (to be published).

V. M. Patel, R. Chellappa, and M. Tistarelli, “Sparse representations and random projections for robust and cancelable biometrics,” in International Conference on Control, Automation, Robotics and Vision, Guangzhou, December, 2010, pp. 1–6.

A. Shrivastava, J. K. Pillai, V. M. Patel, and R. Chellappa, “Learning discriminative dictionaries with partially labeled data,” in IEEE International Conference on Image ProcessingOrlando, 2012, pp. 3113–3116.

Q. Qiu, V. M. Patel, P. Turaga, and R. Chellappa, “Domain adaptive dictionary learning,” in European Conference on Computer Vision (2012), Vol. 7575, pp. 631–645.

S. Shekhar, V. M. Patel, and R. Chellappa, “Synthesis-based recognition of low resolution faces,” in International Joint Conference on Biometrics, Washington, D.C., 2011, pp. 1–6.

V. M. Patel and R. Chellappa, “Sparse representations, compressive sensing and dictionaries for pattern recognition,” in Asian Conference on Pattern Recognition (ACPR), Beijing, 2010.

Pentland, A.

M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cogn. Neurosci. 3, 71–86 (1991).

Phillips, P. J.

Y.-C. Chen, C. S. Sastry, V. M. Patel, P. J. Phillips, and R. Chellappa, “In-plane rotation and scale invariant clustering using dictionaries,” IEEE Trans. Image Process. 22, 2166–2180 (2013).
[CrossRef]

V. M. Patel, W. Tao, S. Biswas, P. J. Phillips, and R. Chellappa, “Dictionary-based face recognition under variable lighting and pose,” IEEE Trans. Inf. Forensics Secur. 7, 954–965 (2012).
[CrossRef]

P. J. Phillips, “Improving face recognition technology,” Computer 44, 84–86 (2011).
[CrossRef]

W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: a literature survey,” ACM Computing Surveys 35, 399–458 (2003).
[CrossRef]

P. J. Phillips, “Matching pursuit filters applied to face identification,” IEEE Trans. Image Process. 7, 1150–1164 (1998).
[CrossRef]

Y.-C. Chen, V. M. Patel, J. K. Pillai, R. Chellappa, and P. J. Phillips, “Dictionary learning from ambiguously labeled data,” in IEEE Conference on Computer Vision and Pattern Recognition, Portland, Oregon, 2013, pp. 353–360.

Y.-C. Chen, V. M. Patel, S. Shekhar, R. Chellappa, and P. J. Phillips, “Video-based face recognition via joint sparse representation,” in IEEE International Conference on Automatic Face and Gesture Recognition, Shanghai, 2013, pp. 1–8.

Y.-C. Chen, V. M. Patel, P. J. Phillips, and R. Chellappa, “Dictionary-based face recognition from video,” in European Conference on Computer Vision, October 2012.

P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, “Overview of the face recognition grand challenge,” in IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005, Vol. 1, pp. 947–954.

Y.-C. Chen, V. M. Patel, R. Chellappa, and P. J. Phillips, “Salient views and view-dependent dictionaries for object recognition,” Comput. Vis. Image Underst. (to be published).

Pillai, J. K.

J. K. Pillai, V. M. Patel, R. Chellappa, and N. Ratha, “Secure and robust iris recognition using random projections and sparse representations,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 1877–1893 (2011).
[CrossRef]

Y.-C. Chen, V. M. Patel, J. K. Pillai, R. Chellappa, and P. J. Phillips, “Dictionary learning from ambiguously labeled data,” in IEEE Conference on Computer Vision and Pattern Recognition, Portland, Oregon, 2013, pp. 353–360.

A. Shrivastava, J. K. Pillai, V. M. Patel, and R. Chellappa, “Learning discriminative dictionaries with partially labeled data,” in IEEE International Conference on Image ProcessingOrlando, 2012, pp. 3113–3116.

Ponce, J.

J. Mairal, F. Bach, and J. Ponce, “Task-driven dictionary learning,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 791–804 (2012).
[CrossRef]

Qiu, Q.

Q. Qiu, V. M. Patel, and R. Chellappa, “Information-theoretic dictionary learning for image classification,” IEEE Trans. Pattern Anal. Mach. Intell. (to be published).

Q. Qiu, V. M. Patel, P. Turaga, and R. Chellappa, “Domain adaptive dictionary learning,” in European Conference on Computer Vision (2012), Vol. 7575, pp. 631–645.

J. Ni, Q. Qiu, and R. Chellappa, “Subspace interpolation via dictionary learning for unsupervised domain adaptation,” in Computer Vision and Pattern Recognition (CVPR), Washington, D.C., 2013, pp. 692–699.

Ratha, N.

J. K. Pillai, V. M. Patel, R. Chellappa, and N. Ratha, “Secure and robust iris recognition using random projections and sparse representations,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 1877–1893 (2011).
[CrossRef]

Rosenfeld, A.

W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: a literature survey,” ACM Computing Surveys 35, 399–458 (2003).
[CrossRef]

Ross Beveridge, J.

P. Jonathon Phillips, P. J. Flynn, J. Ross Beveridge, W. Todd Scruggs, A. J. O’Toole, D. Bolme, K. W. Bowyer, B. A. Draper, G. H. Givens, Y. M. Lui, H. Sahibzada, J. A. Scallan, and S. Weimer, “Overview of the multiple biometrics grand challenge,” in International Conference on Biometrics (Springer, 2009), pp. 705–714.

Rubinstein, R.

R. Rubinstein, A. M. Bruckstein, and M. Elad, “Dictionaries for sparse representation modeling,” Proc. IEEE 98, 1045–1057 (2010).
[CrossRef]

Saenko, K.

K. Saenko, B. Kulis, M. Fritz, and T. Darrell, “Adapting visual category models to new domains,” in European Conference on Computer Vision (Springer-Verlag, 2010), Vol. 6314, pp. 213–226.

Sahibzada, H.

P. Jonathon Phillips, P. J. Flynn, J. Ross Beveridge, W. Todd Scruggs, A. J. O’Toole, D. Bolme, K. W. Bowyer, B. A. Draper, G. H. Givens, Y. M. Lui, H. Sahibzada, J. A. Scallan, and S. Weimer, “Overview of the multiple biometrics grand challenge,” in International Conference on Biometrics (Springer, 2009), pp. 705–714.

Sapiro, G.

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
[CrossRef]

Saragih, J.

P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, “The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops, San Francisco, 2010, pp. 94–101.

Sastry, C. S.

Y.-C. Chen, C. S. Sastry, V. M. Patel, P. J. Phillips, and R. Chellappa, “In-plane rotation and scale invariant clustering using dictionaries,” IEEE Trans. Image Process. 22, 2166–2180 (2013).
[CrossRef]

Sastry, S. S.

J. Wright, A. Y. Yang, A. A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[CrossRef]

Saunders, M.

S. Chen, D. Donoho, and M. Saunders, “Atomic decomposition by basis pursuit,” SIAM J. Sci. Comp 20, 33–61 (1998).
[CrossRef]

Scallan, J. A.

P. Jonathon Phillips, P. J. Flynn, J. Ross Beveridge, W. Todd Scruggs, A. J. O’Toole, D. Bolme, K. W. Bowyer, B. A. Draper, G. H. Givens, Y. M. Lui, H. Sahibzada, J. A. Scallan, and S. Weimer, “Overview of the multiple biometrics grand challenge,” in International Conference on Biometrics (Springer, 2009), pp. 705–714.

Scruggs, T.

P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, “Overview of the face recognition grand challenge,” in IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005, Vol. 1, pp. 947–954.

Sejnowski, T. J.

M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, “Face recognition by independent component analysis,” IEEE Trans. Neural Netw. 13, 1450–1464 (2002).
[CrossRef]

Shan, S.

B. Li, H. Chang, S. Shan, and X. Chen, “Low-resolution face recognition via coupled locality preserving mappings,” IEEE Signal Process. Lett. 17, 20–23 (2010).
[CrossRef]

Shekhar, S.

S. Shekhar, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Joint sparse representation for robust multimodal biometrics recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 36, 113–126 (2014).
[CrossRef]

S. Shekhar, V. M. Patel, and R. Chellappa, “Synthesis-based recognition of low resolution faces,” in International Joint Conference on Biometrics, Washington, D.C., 2011, pp. 1–6.

S. Shekhar, V. M. Patel, H. V. Nguyen, and R. Chellappa, “Generalized domain-adaptive dictionaries,” in Conference on Computer Vision and Pattern Recognition (IEEE, 2013), pp. 361–368.

Y.-C. Chen, V. M. Patel, S. Shekhar, R. Chellappa, and P. J. Phillips, “Video-based face recognition via joint sparse representation,” in IEEE International Conference on Automatic Face and Gesture Recognition, Shanghai, 2013, pp. 1–8.

Shrivastava, A.

A. Shrivastava, H. V. Nguyen, V. M. Patel, and R. Chellappa, “Design of nonlinear discriminative dictionaries for image classification,” in Asian Conference on Computer Vision (ACCV) (Springer-Verlag, 2013), pp. 660–674.

A. Shrivastava, J. K. Pillai, V. M. Patel, and R. Chellappa, “Learning discriminative dictionaries with partially labeled data,” in IEEE International Conference on Image ProcessingOrlando, 2012, pp. 3113–3116.

Snow, S. L.

A. J. O’Toole, J. Harms, S. L. Snow, D. R. Hurst, M. R. Pappas, J. H. Ayyad, and H. Abdi, “Recognizing people from dynamic and static faces and bodies: dissecting identity with a fusion approach,” Vis. Res. 51, 74–83 (2011).
[CrossRef]

Srivastava, A.

P. K. Turaga, A. Veeraraghavan, A. Srivastava, and R. Chellappa, “Statistical computations on Grassmann and Stiefel manifolds for image and video-based recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 2273–2286 (2011).
[CrossRef]

Taheri, S.

S. Taheri, V. M. Patel, and R. Chellappa, “Component-based recognition of faces and facial expressions,” in IEEE Transactions on Affective Computing, October, 2013.

Tao, W.

V. M. Patel, W. Tao, S. Biswas, P. J. Phillips, and R. Chellappa, “Dictionary-based face recognition under variable lighting and pose,” IEEE Trans. Inf. Forensics Secur. 7, 954–965 (2012).
[CrossRef]

Tistarelli, M.

V. M. Patel, R. Chellappa, and M. Tistarelli, “Sparse representations and random projections for robust and cancelable biometrics,” in International Conference on Control, Automation, Robotics and Vision, Guangzhou, December, 2010, pp. 1–6.

Todd Scruggs, W.

P. Jonathon Phillips, P. J. Flynn, J. Ross Beveridge, W. Todd Scruggs, A. J. O’Toole, D. Bolme, K. W. Bowyer, B. A. Draper, G. H. Givens, Y. M. Lui, H. Sahibzada, J. A. Scallan, and S. Weimer, “Overview of the multiple biometrics grand challenge,” in International Conference on Biometrics (Springer, 2009), pp. 705–714.

Turaga, P.

Q. Qiu, V. M. Patel, P. Turaga, and R. Chellappa, “Domain adaptive dictionary learning,” in European Conference on Computer Vision (2012), Vol. 7575, pp. 631–645.

Turaga, P. K.

P. K. Turaga, A. Veeraraghavan, A. Srivastava, and R. Chellappa, “Statistical computations on Grassmann and Stiefel manifolds for image and video-based recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 2273–2286 (2011).
[CrossRef]

P. K. Turaga, A. Veeraraghavan, and R. Chellappa, “Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision,” in IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, 2008, pp. 1–8.

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M. Turk and A. Pentland, “Eigenfaces for recognition,” J. Cogn. Neurosci. 3, 71–86 (1991).

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P. K. Turaga, A. Veeraraghavan, A. Srivastava, and R. Chellappa, “Statistical computations on Grassmann and Stiefel manifolds for image and video-based recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 2273–2286 (2011).
[CrossRef]

P. K. Turaga, A. Veeraraghavan, and R. Chellappa, “Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision,” in IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, 2008, pp. 1–8.

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V. Blanz and T. Vetter, “Face recognition based on fitting a 3d morphable model,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1063–1074 (2003).
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P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” In IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, June, 2001, pp. 511–518.

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J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010, pp. 3360–3367.

Wang, T.

L. Zhang, W.-D. Zhou, P.-C. Chang, J. Liu, Z. Yan, T. Wang, and F.-Z. Li, “Kernel sparse representation-based classifier,” IEEE Trans. Signal Process. 60, 1684–1695 (2012).
[CrossRef]

Weimer, S.

P. Jonathon Phillips, P. J. Flynn, J. Ross Beveridge, W. Todd Scruggs, A. J. O’Toole, D. Bolme, K. W. Bowyer, B. A. Draper, G. H. Givens, Y. M. Lui, H. Sahibzada, J. A. Scallan, and S. Weimer, “Overview of the multiple biometrics grand challenge,” in International Conference on Biometrics (Springer, 2009), pp. 705–714.

Worek, W.

P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, “Overview of the face recognition grand challenge,” in IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005, Vol. 1, pp. 947–954.

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E. J. Candès, X. Li, Y. Ma, and J. Wright, “Robust principal component analysis?” J. ACM 58(3), 1–37 (2011).
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J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
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J. Wright, A. Y. Yang, A. A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[CrossRef]

Yan, S.

J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan, “Sparse representation for computer vision and pattern recognition,” Proc. IEEE 98, 1031–1044 (2010).
[CrossRef]

Yan, Z.

L. Zhang, W.-D. Zhou, P.-C. Chang, J. Liu, Z. Yan, T. Wang, and F.-Z. Li, “Kernel sparse representation-based classifier,” IEEE Trans. Signal Process. 60, 1684–1695 (2012).
[CrossRef]

Yang, A. Y.

J. Wright, A. Y. Yang, A. A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
[CrossRef]

Yang, J.

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010, pp. 3360–3367.

Yang, M.

M. Yang, L. Zhang, X. Feng, and D. Zhang, “Fisher discrimination dictionary learning for sparse representation,” in International Conference on Computer Vision, Barcelona, 2011, pp. 543–550.

Yu, K.

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010, pp. 3360–3367.

Zhang, D.

M. Yang, L. Zhang, X. Feng, and D. Zhang, “Fisher discrimination dictionary learning for sparse representation,” in International Conference on Computer Vision, Barcelona, 2011, pp. 543–550.

Zhang, L.

L. Zhang, W.-D. Zhou, P.-C. Chang, J. Liu, Z. Yan, T. Wang, and F.-Z. Li, “Kernel sparse representation-based classifier,” IEEE Trans. Signal Process. 60, 1684–1695 (2012).
[CrossRef]

M. Yang, L. Zhang, X. Feng, and D. Zhang, “Fisher discrimination dictionary learning for sparse representation,” in International Conference on Computer Vision, Barcelona, 2011, pp. 543–550.

Zhang, Q.

Q. Zhang and B. Li, “Discriminative k-svd for dictionary learning in face recognition,” in IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010.

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W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: a literature survey,” ACM Computing Surveys 35, 399–458 (2003).
[CrossRef]

Zhou, W.-D.

L. Zhang, W.-D. Zhou, P.-C. Chang, J. Liu, Z. Yan, T. Wang, and F.-Z. Li, “Kernel sparse representation-based classifier,” IEEE Trans. Signal Process. 60, 1684–1695 (2012).
[CrossRef]

ACM Computing Surveys (1)

W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face recognition: a literature survey,” ACM Computing Surveys 35, 399–458 (2003).
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P. J. Phillips, “Improving face recognition technology,” Computer 44, 84–86 (2011).
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K. Etemand and R. Chellappa, “Separability-based multiscale basis selection and feature extraction for signal and image classification,” IEEE Trans. Image Process. 7, 1453–1465 (1998).
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H. V. Nguyen, V. M. Patel, N. M. Nasrabadi, and R. Chellappa, “Design of nonlinear kernel dictionaries for object recognition,” IEEE Trans. Image Process. 22, 5123–5135 (2013).
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IEEE Trans. Inf. Forensics Secur. (1)

V. M. Patel, W. Tao, S. Biswas, P. J. Phillips, and R. Chellappa, “Dictionary-based face recognition under variable lighting and pose,” IEEE Trans. Inf. Forensics Secur. 7, 954–965 (2012).
[CrossRef]

IEEE Trans. Neural Netw. (1)

M. S. Bartlett, J. R. Movellan, and T. J. Sejnowski, “Face recognition by independent component analysis,” IEEE Trans. Neural Netw. 13, 1450–1464 (2002).
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IEEE Trans. Pattern Anal. Mach. Intell. (10)

J. Wright, A. Y. Yang, A. A. Ganesh, S. S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 210–227 (2009).
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J. K. Pillai, V. M. Patel, R. Chellappa, and N. Ratha, “Secure and robust iris recognition using random projections and sparse representations,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 1877–1893 (2011).
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J. Mairal, F. Bach, and J. Ponce, “Task-driven dictionary learning,” IEEE Trans. Pattern Anal. Mach. Intell. 34, 791–804 (2012).
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S. Biswas, G. Aggarwal, and R. Chellappa, “Robust estimation of albedo for illumination-invariant matching and shape recovery,” IEEE Trans. Pattern Anal. Mach. Intell. 31, 884–899 (2009).
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V. Blanz and T. Vetter, “Face recognition based on fitting a 3d morphable model,” IEEE Trans. Pattern Anal. Mach. Intell. 25, 1063–1074 (2003).
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P. K. Turaga, A. Veeraraghavan, A. Srivastava, and R. Chellappa, “Statistical computations on Grassmann and Stiefel manifolds for image and video-based recognition,” IEEE Trans. Pattern Anal. Mach. Intell. 33, 2273–2286 (2011).
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L. Zhang, W.-D. Zhou, P.-C. Chang, J. Liu, Z. Yan, T. Wang, and F.-Z. Li, “Kernel sparse representation-based classifier,” IEEE Trans. Signal Process. 60, 1684–1695 (2012).
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Other (29)

P. Lucey, J. F. Cohn, T. Kanade, J. Saragih, Z. Ambadar, and I. Matthews, “The extended cohn-kanade dataset (ck+): a complete dataset for action unit and emotion-specified expression,” in IEEE Conference on Computer Vision and Pattern Recognition Workshops, San Francisco, 2010, pp. 94–101.

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Y. Hu, A. S. Mian, and R. Owens, “Sparse approximated nearest points for image set classification,” in Conference on Computer Vision and Pattern Recognition (IEEE, 2011), pp. 27–40.

P. Jonathon Phillips, P. J. Flynn, J. Ross Beveridge, W. Todd Scruggs, A. J. O’Toole, D. Bolme, K. W. Bowyer, B. A. Draper, G. H. Givens, Y. M. Lui, H. Sahibzada, J. A. Scallan, and S. Weimer, “Overview of the multiple biometrics grand challenge,” in International Conference on Biometrics (Springer, 2009), pp. 705–714.

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A. Shrivastava, J. K. Pillai, V. M. Patel, and R. Chellappa, “Learning discriminative dictionaries with partially labeled data,” in IEEE International Conference on Image ProcessingOrlando, 2012, pp. 3113–3116.

Q. Qiu, V. M. Patel, P. Turaga, and R. Chellappa, “Domain adaptive dictionary learning,” in European Conference on Computer Vision (2012), Vol. 7575, pp. 631–645.

S. Shekhar, V. M. Patel, H. V. Nguyen, and R. Chellappa, “Generalized domain-adaptive dictionaries,” in Conference on Computer Vision and Pattern Recognition (IEEE, 2013), pp. 361–368.

J. Ni, Q. Qiu, and R. Chellappa, “Subspace interpolation via dictionary learning for unsupervised domain adaptation,” in Computer Vision and Pattern Recognition (CVPR), Washington, D.C., 2013, pp. 692–699.

Y.-C. Chen, V. M. Patel, R. Chellappa, and P. J. Phillips, “Salient views and view-dependent dictionaries for object recognition,” Comput. Vis. Image Underst. (to be published).

K. Engan, S. O. Aase, and J. H. Husoy, “Method of optimal directions for frame design,” in IEEE International Conference on Acoustic, Speech, Signal Processing, Phoenix, 1999, Vol. 5, pp. 2443–2446.

A. Shrivastava, H. V. Nguyen, V. M. Patel, and R. Chellappa, “Design of nonlinear discriminative dictionaries for image classification,” in Asian Conference on Computer Vision (ACCV) (Springer-Verlag, 2013), pp. 660–674.

Q. Zhang and B. Li, “Discriminative k-svd for dictionary learning in face recognition,” in IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010.

M. Yang, L. Zhang, X. Feng, and D. Zhang, “Fisher discrimination dictionary learning for sparse representation,” in International Conference on Computer Vision, Barcelona, 2011, pp. 543–550.

Q. Qiu, V. M. Patel, and R. Chellappa, “Information-theoretic dictionary learning for image classification,” IEEE Trans. Pattern Anal. Mach. Intell. (to be published).

M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing (Springer, 2010).

V. M. Patel, R. Chellappa, and M. Tistarelli, “Sparse representations and random projections for robust and cancelable biometrics,” in International Conference on Control, Automation, Robotics and Vision, Guangzhou, December, 2010, pp. 1–6.

V. M. Patel and R. Chellappa, “Sparse representations, compressive sensing and dictionaries for pattern recognition,” in Asian Conference on Pattern Recognition (ACPR), Beijing, 2010.

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K. Saenko, B. Kulis, M. Fritz, and T. Darrell, “Adapting visual category models to new domains,” in European Conference on Computer Vision (Springer-Verlag, 2010), Vol. 6314, pp. 213–226.

S. Biswas and R. Chellappa, “Pose-robust albedo estimation from a single image,” in IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010.

J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, 2010, pp. 3360–3367.

S. Shekhar, V. M. Patel, and R. Chellappa, “Synthesis-based recognition of low resolution faces,” in International Joint Conference on Biometrics, Washington, D.C., 2011, pp. 1–6.

P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, and W. Worek, “Overview of the face recognition grand challenge,” in IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005, Vol. 1, pp. 947–954.

S. Taheri, V. M. Patel, and R. Chellappa, “Component-based recognition of faces and facial expressions,” in IEEE Transactions on Affective Computing, October, 2013.

Y.-C. Chen, V. M. Patel, P. J. Phillips, and R. Chellappa, “Dictionary-based face recognition from video,” in European Conference on Computer Vision, October 2012.

P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” In IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, June, 2001, pp. 511–518.

Y.-C. Chen, V. M. Patel, S. Shekhar, R. Chellappa, and P. J. Phillips, “Video-based face recognition via joint sparse representation,” in IEEE International Conference on Automatic Face and Gesture Recognition, Shanghai, 2013, pp. 1–8.

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Figures (13)

Fig. 1.
Fig. 1.

Examples of the original images (first column) and the corresponding relighted images with different light source directions from the PIE data set [31].

Fig. 2.
Fig. 2.

Poserobust albedo estimation. Left column: original input images. Middle column: recovered albedo maps corresponding to frontal face images. Right column: pose normalized relighted images [28].

Fig. 3.
Fig. 3.

Typical image in remote face recognition.

Fig. 4.
Fig. 4.

Overview of the dictionary-based low-resolution face recognition [36].

Fig. 5.
Fig. 5.

Recognition rates for FRGC data with probes at low resolutions [36].

Fig. 6.
Fig. 6.

Facial component separation. (a) Original face image, (b) is viewed as the superposition of a neutral component, and (c) with a component containing the expression [39].

Fig. 7.
Fig. 7.

Component-based recognition of faces and facial expressions algorithm overview [39].

Fig. 8.
Fig. 8.

Effects of various expressions on the face recognition results on the CK+ dataset using S3 set-up. Each bar shows the face recognition rate we obtain when all the faces with corresponding expressions are kept out for testing and the rest are used for training [39].

Fig. 9.
Fig. 9.

DFRV algorithm overview [43].

Fig. 10.
Fig. 10.

ROC curves of FOCS experiments on UT-Dallas video [43].

Fig. 11.
Fig. 11.

Dictionary-based face recognition from ambiguously labeled data algorithm overview [53].

Fig. 12.
Fig. 12.

Overview of domain adaptive latent space dictionary learning framework [56].

Fig. 13.
Fig. 13.

Examples of pose-aligned images. Synthesis in various conditions demonstrate the robustness of the domain adaptive dictionary learning method [56].

Tables (4)

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Table 1. Identification Rate (%) on the Extended YaleB Face Dataset [24]

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Table 2. Identification Rates of Leave-One-Out Testing Experiments on the FOCS UT-Dallas Walking Videos; DFRV Method Performs Best [43]

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Table 3. Identification Rates of Leave-One-Out Testing Experiments on the MBGC Walking Videos [43]

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Table 4. Identification Rates of “SD versus HD” and “HD versus SD” Experiments on the MBGC Walking Video Subset S2a

Equations (54)

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D=[d1,,dN]Rd×N.
xt=argminxx0subjecttoyt=Dx,
xt=argminxx1subjecttoyt=Dx,
xp=(j=1d|xj|p)1p.
yt=Dx+nforn2<ϵ.
xt=argminxx1subjecttoytDx2<ϵ.
(D^,X^)=argminD,XYDXF2s.t.xi0T0i,
i=1,,n,minγiyiDxi22s.t.xi0T0.
Y=[Y1,,YC]Rd×N=[y11,,yN11|y12,,yN22||y1C,,yNCC][y1,y2,,yN],
yt=c=1Ci=1Ncxicyic
yt=Yx,
x=[x11,,xN11|x12,,xN22||x1C,,xNCC]T[x1,x2,,xN]T
xt=argminxx1subjecttoytYx2ϵ
xt=argminxytYx2+λx1,
ec=ytYcxtc2,
c*=classofyt=argmincec.
(D^i,X^i)=argminDi,XiYiDiXiF2s.t.xj0T0j,
Pi=Di(DiTDi)1DiT.
y^i=Piyt=Diαi
ri(yt)=yty^i=(IPi)yt,
αi=(DiTDi)1DiTyt
d=identity(yt)=argminiri(yt)2.
yi,j=ρi,jmax(ni,jTs,0),
yi,j=ρi,jmax(ni,jTs,0)ρi,jni,jTs.
ρi,j(0)=yi,jni,j(0)·s(0),
ρi,j(0)=ρi,jni,j·sni,j(0)·s(0)=ρi,j+ni,j·sni,j(0)·s(0)ni,j(0)·s(0)ρi,j,=ρi,j+ωi,j,
ωi,j=ni,j·sni,j(0)·s(0)ni,j(0)·s(0)ρi,j.
ρ¯i,j=yi,jn¯i,jΘ¯·s¯,
ρ¯i,j=ρi,jhi,j+ωi,j,
wi,j=n¯i,jΘ·sn¯i,jΘ·s¯n¯i,jΘ¯·s¯ρi,j,
hi,j=n¯i,jΘ·s¯n¯i,jΘ¯·s¯,
minD,XYDXF2+λΨ(X),
y=yn+ye.
x^n,x^e=argminxn,xeλxn1+λxe1+12yDnxnDexe22.
Gj,ki=[gj,k,1igj,k,2i],
(D^j,ki,G^j,ki)=argminDj,ki,Xj,kiGj,kiDj,kiXj,kiF2s.t.xl0T0,l,
Dji=[Dj,1iDj,2iDj,ki].
Q(m)=k=1KQk(m).
Qk(m)=[qk,1(m)qk,2(m)qk,nk(m)],
p^=argminpqk,l(m)D(p)D(p)qk,l(m)2,
p*=argmaxp(k=1KwkCp,k),
R(m,p)=mink{1,2,,K}Rk(m,p),
Rk(m,p)minl{1,2,,nk}qk,l(m)D(p)D(p)qk,l(m)2.
pi,j=0ifjLi,i=1,,N,pi,j(0,1]ifjLi,i=1,,N.
pi,j(t)=αj(t)exp(eij(t)σ2)cLiαc(t)exp(eic(t)σ2),
eij(t)=yiDj(t)D¯j(t)yi2
ji=argmaxcLipi,c(t).
(Dj(t+1),Xj(t+1))=argminD,XCj(t+1)DXF2,subjecttoxi0T0,i,
C1(D,W1,W2,X1,X2)=W1Y1DX1F2+W2Y2DX2F2,
C2(W1,W2)=Y1W1TW1Y1F2+Y2W2TW2Y2F2.
C1(D,W˜,X˜)=W˜Y˜DX˜F2,
C2(W˜)=trace((W˜Y˜)(W˜Y˜)T),
W˜=[W1W2],Y˜=(Y100Y2),andX˜=[X1X2].
{D*,W˜*,X˜*}=argminD,W˜,X˜C1(D,P˜,X˜)+λC2(W˜),s.t.WiWiT=I,i=1,2and x˜j0T0,j,

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