http://iet.metastore.ingenta.com
1887

A comparison of deep multilayer networks and Markov random field matching models for face recognition in the wild

A comparison of deep multilayer networks and Markov random field matching models for face recognition in the wild

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Computer Vision — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Robustness to a diverse range of image transformations and distortions has been an everlasting goal of visual pattern recognition. While there have been a huge number of efforts to advance the state-of-the art in this direction over the last decades, two prominent outstanding schemes, among others, are deep multilayer architectures and graphical models, providing some degree of robustness to undesired image perturbations. In this study, the authors aim at shedding some light on the underlying concepts, mechanisms, strengths and potentials of each methodology while discussing their relative merits from a practical point of view. In particular, they discuss the underlying motivations for the construction of deep multilayer architectures and undirected graphical models, also known as Markov random fields. The principles in the construction of each architecture, how invariance properties are achieved in each approach, the efficiency of each approach in terms of computations required during train and test as well as the degree of human labour required in each approach are discussed. Finally, an experimental comparison of the performances of the two frameworks is performed on a challenging problem of face recognition in unconstrained settings in the presence of a wide range of undesirable visual perturbations.

References

    1. 1)
      • 1. Duda, R.O., Hart, P.E., Stork, D.G.: ‘Pattern classification’ (New York, 2001, 2nd edn.).
    2. 2)
      • 2. Li, S.: ‘Markov random field modeling in image analysis’ (Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2001).
    3. 3)
      • 3. Blake, A., Kohli, P., Rother, C. (Eds.): ‘Markov random fields for vision and image processing’ (MIT Press, 2011).
    4. 4)
      • 4. Arashloo, S.R., Kittler, J.: ‘Energy normalization for pose-invariant face recognition based on mrf model image matching’, IEEE Trans. Pattern Anal. Mach. Intell., 2011, 33, (6), pp. 12741280.
    5. 5)
      • 5. Arashloo, S.R., Kittler, J.: ‘Pose-invariant face matching using mrf energy minimization framework’. Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR), 2009 (LNCS, 5681), pp. 5669.
    6. 6)
      • 6. Arashloo, S.R., Kittler, J.: ‘Fast pose invariant face recognition using super coupled multiresolution Markov random fields on a gpu’, Pattern Recognit. Lett., 2014, 10, (11), pp. 23962407.
    7. 7)
      • 7. Bengio, Y.: ‘Learning deep architectures for ai’, Found. Trends Mach. Learn., 2009, 2, (1), pp. 1127.
    8. 8)
      • 8. Simard, P., Victorri, B., LeCun, Y., et al: ‘Tangent prop – a formalism for specifying selected invariances in an adaptive network’. Neural Information Processing Systems (NIPS), 1991, pp. 895903.
    9. 9)
      • 9. Bishop, C.M.: ‘Neural networks for pattern recognition’ (Oxford University Press, 1995).
    10. 10)
      • 10. Lecun, Y., Bottou, L., Bengio, Y., et al: ‘Gradient-based learning applied to document recognition’, Proc. IEEE, 1998, 86, pp. 22782324.
    11. 11)
      • 11. Fukushima, K., Miyake, S., Ito, T.: ‘Neocognitron: a neural network model for a mechanism of visual pattern recognition’, IEEE Trans. Syst. Man Cybern., 1983, SMC-13, pp. 826834.
    12. 12)
      • 12. Fukushima, K.: ‘Neocognitron: a hierarchical neural network capable of visual pattern recognition’, Neural Netw., 1988, 1, pp. 119130.
    13. 13)
      • 13. Sun, Y., Wang, X., Tang, X.: ‘Hybrid deep learning for face verification’. IEEE Int. Conf. on Computer Vision (ICCV), 2013, December 2013, pp. 14891496.
    14. 14)
      • 14. Yaniv, T., Ming, Y., MarcAurelio, R., et al: ‘Deepface: closing the gap to human-level performance in face verification’. Computer Vision and Pattern Recognition (CVPR), 2014.
    15. 15)
      • 15. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ‘ImageNet Classification with Deep Convolutional Neural Networks’, in Bartlett, P.L., Pereira, F.C.N., Burges, C.J.C., et al (eds.), ‘NIPS’. 2012, pp. 11061114.
    16. 16)
      • 16. Shekhovtsov, A., Kovtun, I., Hlavac, V.: ‘Efficient mrf deformation model for non-rigid image matching’, Comput. Vis. Image Underst., 2008, 112, pp. 9199.
    17. 17)
      • 17. Geman, S., Geman, D.: ‘Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images’, IEEE Trans. Pattern Anal. Mach. Intell., 1984, PAMI-6, (6), pp. 721741.
    18. 18)
      • 18. Arashloo, S.R., Kittler, J.: ‘Efficient processing of mrfs for unconstrained-pose face recognition’. IEEE Sixth Int. Conf. on Biometrics: Theory, Applications and Systems (BTAS), 2013, September 2013, pp. 18.
    19. 19)
      • 19. Konrad, J., Dubois, E.: ‘Bayesian estimation of motion vector fields’, IEEE Trans. Pattern Anal. Mach. Intell., 1992, 14, (9), pp. 910927.
    20. 20)
      • 20. Heitz, F., Bouthemy, P.: ‘Multimodal estimation of discontinuous optical flow using Markov random fields’, IEEE Trans. Pattern Anal. Mach.Intell., 1993, 15, (12), pp. 12171232.
    21. 21)
      • 21. Boykov, Y., Veksler, O., Zabih, R.: ‘Fast approximate energy minimization via graph cuts’. The Proc. of the Seventh IEEE Int. Conf. on Computer Vision, 1999, 1999, vol. 1, pp. 377384.
    22. 22)
      • 22. Kumar, M., Torr, P., Zisserman, A.: ‘Learning layered motion segmentations of video’. Tenth IEEE Int. Conf. on Computer Vision, 2005 (ICCV 2005), 17–21 October 2005, vol. 1, pp. 3340.
    23. 23)
      • 23. Jiang, H., Drew, M., Li, Z.: ‘Matching by linear programming and successive convexification’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (6), pp. 959975.
    24. 24)
      • 24. Tobias, H., Sascha, M., Hans-Peter, M., Ivo, W.A.: ‘Shape-Guided Deformable Model with Evolutionary Algorithm Initialization for 3D Soft Tissue Segmentation’, in Karssemeijer, N., Lelieveldt, B. (Eds.): Information Processing in Medical Imaging: 20th International Conference, IPMI 2007, Kerkrade, The Netherlands, July 2–6, 2007. (Springer Berlin Heidelberg, 2007), pp. 112.
    25. 25)
      • 25. Glocker, B., Komodakis, N., Tziritas, G., et al: ‘Dense image registration through mrfs and efficient linear programming’, Med. Image Anal., 2008, 12, (6), pp. 731741, (special issue on Information Processing in Medical Imaging 2007).
    26. 26)
      • 26. Komodakis, N., Tziritas, G., Paragios, N.: ‘Fast, approximately optimal solutions for single and dynamic mrfs’. Computer Vision and Pattern Recognition (CVPR), 2007.
    27. 27)
      • 27. Felzenszwalb, P., Huttenlocher, D.: ‘Efficient belief propagation for early vision’. Proc. of the 2004 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2004 (CVPR 2004), 2004, vol. 1, pp. I-261I-268.
    28. 28)
      • 28. Duchenne, O., Joulin, A., Ponce, J.: ‘A graph-matching kernel for object categorization’. Int. Conf. on Computer Vision (ICCV), 2011, pp. 17921799.
    29. 29)
      • 29. Keysers, D., Deselaers, T., Gollan, C., et al: ‘Deformation models for image recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (8), pp. 14221435.
    30. 30)
      • 30. Liu, K., Zhang, J., Huang, K., et al: ‘Deformable object matching via deformation decomposition based 2d label mrf’, IEEE Conference Computer Vision and Pattern Recognition (CVPR), June 2014.
    31. 31)
      • 31. Zhou, F., la Torre, F.D.: ‘Deformable graph matching’. Computer Vision and Pattern Recognition (CVPR), 2013, pp. 29222929.
    32. 32)
      • 32. Wiskott, L., Fellous, J.-M., Krger, N., et al: ‘Face recognition by elastic bunch graph matching’, IEEE Trans. Pattern Anal. Mach. Intell., 1997, 19, (7), pp. 775779.
    33. 33)
      • 33. Komodakis, N., Paragios, N., Tziritas, G.: ‘Mrf optimization via dual decomposition: message-passing revisited’. Int. Conf. on Computer Vision (ICCV), 2007, pp. 18.
    34. 34)
      • 34. Pearl, J.: ‘Probabilistic reasoning in intelligent systems: networks of plausible inference’ (Morgan Kaufmann, 1988).
    35. 35)
      • 35. Wainwright, M., Jordan, M.: ‘Graphical models, exponential families, and variational inference’ (Now Publishers Inc., Hanover, MA, USA, 2008), vol. 1, (1–2).
    36. 36)
      • 36. Besag, J.: ‘On the statistical analysis of dirty pictures’, J. R. Stat. Soc., 1986, 48, (3), pp. 259302.
    37. 37)
      • 37. Kirkpatrick, S., , C.D.G.Jr., Vecchi, M.P.: ‘Optimization by simulated annealing’, Science, 1983, 220, pp. 671680.
    38. 38)
      • 38. Kolmogorov, V.: ‘Convergent tree-reweighted message passing for energy minimization’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (10), pp. 15681583.
    39. 39)
      • 39. Werner, T.: ‘High-arity interactions, polyhedral relaxations, and cutting plane algorithm for soft constraint optimisation (MAP-MRF)’. Proc. of the 2008 IEEE Computer Society Conf. on Computer Vision and Pattern Recognition (CVPR 2008), Madison, USA, June 2008, pp. 109116.
    40. 40)
      • 40. Ramalingam, S., Kohli, P., Alahari, K., et al: ‘Exact inference in multi-label crfs with higher order cliques’. Computer Vision and Pattern Recognition (CVPR), 2008.
    41. 41)
      • 41. Boykov, Y., Veksler, O., Zabih, R.: ‘Fast approximate energy minimization via graph cuts’, IEEE Trans. Pattern Anal. Mach. Intell., 2001, 23, (11), pp. 12221239.
    42. 42)
      • 42. Werner, T.: ‘A linear programming approach to max-sum problem: a review’, IEEE Trans. Pattern Anal. Mach. Intell., 2007, 29, (7), pp. 11651179.
    43. 43)
      • 43. Gidas, B.: ‘A renormalization group approach to image processing problems’, PAMI, 1989, 11, (2), pp. 164180.
    44. 44)
      • 44. Bober, M., Petrou, M., Kittler, J.: ‘Nonlinear motion estimation using the supercoupling approach’, IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20, (5), pp. 550555.
    45. 45)
      • 45. Arashloo, S.R., Kittler, J., Christmas, W.J.: ‘Pose-invariant face recognition by matching on multi-resolution mrfs linked by supercoupling transform’, Comput. Vis. Image Underst., 2011, 115, (7), pp. 10731083, (special issue on Graph-based Representations in Computer Vision).
    46. 46)
      • 46. Arashloo, S., Kittler, J.: ‘Class-specific kernel fusion of multiple descriptors for face verification using multiscale binarised statistical image features’, IEEE Trans. Inf. Forensics Sec., 2014, 9, (12), pp. 21002109.
    47. 47)
      • 47. Chan, C.-H., Kittler, J., Messer, K.: ‘Multi-scale local binary pattern histograms for face recognition’. Proc. of Int. Conf. on Biometrics, August 2007, pp. 809818.
    48. 48)
      • 48. Chan, C.H., Tahir, M., Kittler, J., et al: ‘Multiscale local phase quantization for robust component-based face recognition using kernel fusion of multiple descriptors’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (5), pp. 11641177.
    49. 49)
      • 49. Kannala, J., Rahtu, E.: ‘Bsif: binarized statistical image features’. Proc. 21st Int. Conf. on Pattern Recognition (ICPR 2012), Tsukuba, Japan, 2012, pp. 13631366.
    50. 50)
      • 50. Socher, R., Pennington, J., Huang, E.H., et al: ‘Semi-supervised recursive autoencoders for predicting sentiment distributions’. Empirical Methods on Natural Language Processing (EMNLP), 2011, pp. 151161.
    51. 51)
      • 51. Bordes, A., Glorot, X., Weston, J., et al: ‘Joint learning of words and meaning representations for open-text semantic parsing’. Artificial Intelligence and Statistics (AISTATS), 2012, vol. 22, pp. 127135.
    52. 52)
      • 52. Ciresan, D.C., Meier, U., Schmidhuber, J.: ‘Multi-column deep neural networks for image classification’. Computer Vision and Pattern Recognition (CVPR), 2012, pp. 36423649.
    53. 53)
      • 53. Ranzato, M.A., Huang, F.J., Boureau, Y.L., et al: ‘Unsupervised learning of invariant feature hierarchies with applications to object recognition’. IEEE Conf. on Computer Vision and Pattern Recognition, 2007 (CVPR ’07), 2007, pp. 18.
    54. 54)
      • 54. Sun, Y., Wang, X., Tang, X.: ‘Deep convolutional network cascade for facial point detection’. Computer Vision and Pattern Recognition (CVPR), 2013, pp. 34763483.
    55. 55)
      • 55. Luo, P., Wang, X., Tang, X.: ‘A deep sum-product architecture for robust facial attributes analysis’. Int. Conf. on Computer Vision (ICCV), 2013, pp. 28642871.
    56. 56)
      • 56. Chen, F., Yu, H., Hu, R., et al: ‘Deep learning shape priors for object segmentation’. Computer Vision and Pattern Recognition (CVPR), 2013, pp. 18701877.
    57. 57)
      • 57. Ouyang, W., Wang, X.: ‘Joint deep learning for pedestrian detection’. Int. Conf. on Computer Vision (ICCV), 2013.
    58. 58)
      • 58. Weinzaepfel, P., Revaud, J., Harchaoui, Z., et al: ‘Deepflow: large displacement optical flow with deep matching’. Int. Conf. on Computer Vision (ICCV), 2013, pp. 13851392.
    59. 59)
      • 59. Titov, I., Henderson, J.: ‘Constituent parsing with incremental sigmoid belief networks’. The Association for Computational Linguistics (ACL), 2007.
    60. 60)
      • 60. Saul, L.K., Jaakkola, T., Jordan, M.I.: ‘Mean field theory for sigmoid belief networks’, CoRR, 1996, cs.AI/9603102.
    61. 61)
      • 61. Hinton, G.E., Osindero, S., Teh, Y.W.: ‘A fast learning algorithm for deep belief nets’, Neural Comput., 2006, 18, (7), pp. 15271554.
    62. 62)
      • 62. Bishop, C.: ‘Pattern recognition and machine learning’ (Springer, New York, 2006), vol. 4.
    63. 63)
      • 63. Hubel, D., Wiesel, T.N.: ‘Receptive fields, binocular interaction, and functional architecture in the cat's visual cortex’, J. Physiol., 1962, 160, pp. 106154.
    64. 64)
      • 64. Huang, G.B., Lee, H., Learned-Miller, E.G.: ‘Learning hierarchical representations for face verification with convolutional deep belief networks’. Computer Vision and Pattern Recognition (CVPR), 2012, pp. 25182525.
    65. 65)
      • 65. Sun, Y., Wang, X., Tang, X.: ‘Deep learning face representation by joint identification-verification’, CoRR, 2014, abs/1406.4773.
    66. 66)
      • 66. Huang, G., Mattar, M., Berg, T., et al: ‘Labeled faces in the wild, a database for studying face recognition in unconstrained environments’, 2008, (faces in real life images workshop in ECCV).
    67. 67)
      • 67. del Solar, J.R., Verschae, R., Correa, M.: ‘Recognition of faces in unconstrained environments: a comparative study’, EURASIP J. Adv. Signal Process., 2009, 2009, pp. 119.
    68. 68)
      • 68. Seo, H.J., Milanfar, P.: ‘Face verification using the lark representation’, IEEE Trans. Inf. Forensics Sec., 2011, 6, (4), pp. 12751286.
    69. 69)
      • 69. Sharma, G., ul Hussain, S., Jurie, F.: ‘Local higher-order statistics (lhs) for texture categorization and facial analysis’. Proc. of the 12th European Conf. on Computer Vision – Volume Part VII, ECCV'12, Berlin, Heidelberg, 2012, pp. 112.
    70. 70)
      • 70. Yi, D., Lei, Z., Li, S.: ‘Towards pose robust face recognition’. IEEE Computer Vision and Pattern Recognition, 2013.
    71. 71)
      • 71. Turk, M.A., Pentland, A.P.: ‘Face recognition using eigenfaces’. Proc. Conf. on Computer Vision and Pattern Recognition, 1991, pp. 586591.
    72. 72)
      • 72. Nowak, E., Jurie, F.: ‘Learning visual similarity measures for comparing never seen objects’. IEEE Conf. on Computer Vision and Pattern Recognition, 2007 (CVPR ’07), June 2007, pp. 18.
    73. 73)
      • 73. Wolf, L., Hassner, T., Taigman, Y.: ‘Descriptor based methods in the wild’. Post ECCV workshop on Faces in Real-Life Images: Detection, Alignment, and Recognition, 2008.
    74. 74)
      • 74. Sanderson, C., Lovell, B.C.: ‘Multi-region probabilistic histograms for robust and scalable identity inference’. ICB, 2009 (LNCS, 5558), pp. 199208.
    75. 75)
      • 75. Pinto, N., DiCarlo, J.J., Cox, D.D.: ‘How far can you get with a modern face recognition test set using only simple features?’. IEEE Computer Vision and Pattern Recognition, 2009.
    76. 76)
      • 76. Li, H., Hua, G., Lin, Z., et al: ‘Probabilistic elastic matching for pose variant face verification’. IEEE Computer Vision and Pattern Recognition, 2013.
    77. 77)
      • 77. Simonyan, K., Parkhi, O., Vedaldi, A., et al: ‘Fisher vector faces in the wild’. British Machine Vision Conf. (BMVC), 2013.
    78. 78)
      • 78. Hu, J., Lu, J., Tan, Y.-P.: ‘Discriminative deep metric learning for face verification in the wild’. The IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2014.
    79. 79)
      • 79. Huang, G.B., Jones, M.J., Learned-miller, E.: ‘Lfw results using a combined nowak plus merl recognizer’. Faces in Real-Life Images Workshop in European Conf. on Computer Vision (ECCV), 2008.
    80. 80)
      • 80. Guillaumin, M., Verbeek, J., Schmid, C.: ‘Is that you? metric learning approaches for face identification’. Int. Conf. on Computer Vision, September 2009, pp. 498505. Available at http://www.lear.inrialpes.fr/pubs/2009/GVS09.
    81. 81)
      • 81. Taigman, Y., Wolf, L., Hassner, T.: ‘Multiple one-shots for utilizing class label information’. British Machine Vision Association (BMVC), 2009.
    82. 82)
      • 82. Nair, V., Hinton, G.E.: ‘Rectified linear units improve restricted Boltzmann machines’. Int. Conf. on Machine Learning (ICML), 2010, pp. 807814.
    83. 83)
      • 83. Cao, Z., Yin, Q., Tang, X., et al: ‘Face recognition with learning-based descriptor’. Computer Vision and Pattern Recognition (CVPR), 2010, pp. 27072714.
    84. 84)
      • 84. Nguyen, H.V., Bai, L.: ‘Cosine similarity metric learning for face verification’. Asian Conf. on Computer Vision (ACCV), 2010 (LNCS, 6493), pp. 709720.
    85. 85)
      • 85. Cox, D.D., Pinto, N.: ‘Beyond simple features: a large-scale feature search approach to unconstrained face recognition’. Face and Gesture Recognition (FG), 2011, pp. 815.
    86. 86)
      • 86. Ying, Y., Li, P.: ‘Distance metric learning with eigenvalue optimization’, J. Mach. Learn. Res., 2012, 13, pp. 126.
    87. 87)
      • 87. Cui, Z., Li, W., Xu, D., et al: ‘Fusing robust face region descriptors via multiple metric learning for face recognition in the wild’. Computer Vision and Pattern Recognition (CVPR), 2013, pp. 35543561.
    88. 88)
      • 88. Cao, Q., Ying, Y., Li, P.: ‘Similarity metric learning for face recognition’. The IEEE Int. Conf. on Computer Vision (ICCV), December 2013.
    89. 89)
      • 89. Barkan, O., Weill, J., Wolf, L., et al: ‘Fast high dimensional vector multiplication face recognition’. The IEEE Int. Conf. on Computer Vision (ICCV), December 2013.
    90. 90)
      • 90. Kumar, N., Berg, A.C., Belhumeur, P.N., et al: ‘Attribute and simile classifiers for face verification’. Int. Conf. on Computer Vision (ICCV), 2009, pp. 365372.
    91. 91)
      • 91. Yin, Q., Tang, X., 0001, J.S.: ‘An associate-predict model for face recognition’. Computer Vision and Pattern Recognition (CVPR), 2011, pp. 497504.
    92. 92)
      • 92. Berg, T., Belhumeur, P.N.: ‘Tom-vs-Pete classifiers and identity-preserving alignment for face verification’. British Machine Vision Conf. (BMVC), 2012, pp. 111.
    93. 93)
      • 93. Chen, D., Cao, X., Wang, L., et al: ‘Bayesian face revisited: a joint formulation’. European Conf. on Computer Vision (ECCV), 2012 (LNCS, 7574), pp. 566579.
    94. 94)
      • 94. Chen, D., Cao, X., Wen, F., et al: ‘Blessing of dimensionality: High-dimensional feature and its efficient compression for face verification’. Computer Vision and Pattern Recognition (CVPR), 2013, pp. 30253032.
    95. 95)
      • 95. Lei, Z., Pietikainen, M., Li, S.: ‘Learning discriminant face descriptor’, IEEE Trans. Pattern Anal. Mach. Intell., 2014, 36, (2), pp. 289302.
    96. 96)
      • 96. Cao, X., Wipf, D., Wen, F., et al: ‘A practical transfer learning algorithm for face verification’. Int. Conf. on Computer Vision (ICCV), 2013.
    97. 97)
      • 97. Taigman, Y., Wolf, L.: ‘Leveraging billions of faces to overcome performance barriers in unconstrained face recognition’, CoRR, 2011, abs/1108.1122.
    98. 98)
      • 98. Fan, H., Cao, Z., Jiang, Y., et al: ‘Learning deep face representation’, CoRR, 2014, abs/1403.2802.
    99. 99)
      • 99. Taigman, Y., Yang, M., Ranzato, M., et al: ‘Deepface: closing the gap to human-level performance in face verification’. Conf. on Computer Vision and Pattern Recognition (CVPR), 2014.
    100. 100)
      • 100. Sun, Y., Wang, X., Tang, X.: ‘Hybrid deep learning for face verification’. The IEEE Int. Conf. on Computer Vision (ICCV), December 2013.
    101. 101)
      • 101. Berg, T., Belhumeur, P.N.: ‘Poof: Part-based one-vs.-one features for fine-grained categorization, face verification, and attribute estimation’. Computer Vision and Pattern Recognition (CVPR), 2013, pp. 955962.
    102. 102)
      • 102. Zhu, Z., Luo, P., Wang, X., et al: ‘Recover canonical-view faces in the wild with deep neural networks’, CoRR, 2014, abs/1404.3543.
    103. 103)
      • 103. Sun, Y., Wang, X., Tang, X.: ‘Deep learning face representation from predicting 10,000 classes’. The IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2014.
    104. 104)
      • 104. Lu, C., Tang, X.: ‘Surpassing human-level face verification performance on lfw with Gaussian face’, CoRR, 2014, abs/1404.3840.
    105. 105)
      • 105. Chopra, S., Hadsell, R., Lecun, Y.: ‘Learning a similarity metric discriminatively, with application to face verification’. Proc. of Computer Vision and Pattern Recognition Conf., 2005, pp. 539546.
    106. 106)
      • 106. Zhu, Z., Luo, P., Wang, X., et al: ‘Deep learning identity-preserving face space’. IEEE Int. Conf. on Computer Vision (ICCV), 2013, December 2013.
    107. 107)
      • 107. Kan, M., Shan, S., Chang, H., et al: ‘Stacked progressive auto-encoders (spae) for face recognition across poses’. The IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2014, pp. 18831890.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2015.0222
Loading

Related content

content/journals/10.1049/iet-cvi.2015.0222
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address