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Reference-Based Pose-Robust Face Recognition

Chapter

Abstract

Despite recent advancement in face recognition technology, practical pose-robust face recognition remains a challenge. To meet this challenge, this chapter introduces reference-based similarity where the similarity between a face image and a set of reference individuals (the “reference set”) defines the reference-based descriptor for a face image. Recognition is performed using the reference-based descriptors of probe and gallery images. The dimensionality of the face descriptor generated by the accompanying face recognition algorithm is reduced to the number of individuals in the reference set. The proposed framework is a generalization of previous recognition methods that use indirect similarity and reference-based descriptors. The effectiveness of the proposed algorithm is shown by transforming multiple variations of the standard, yet powerful, local binary patterns descriptor into pose-robust face descriptors. Results are shown on several publicly available face databases. The proposed approach achieves good accuracy as compared to popular state-of-the-art algorithms, and it is computationally efficient due to its compatibility with orthogonal transform based indexing algorithms.

Keywords

Face Recognition Discrete Cosine Transform Face Image Local Binary Pattern Reference Individual 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    T. Ahonen, A. Hadid, M. Pietikainen, Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)CrossRefzbMATHGoogle Scholar
  2. 2.
    L. An, B. Bhanu, S. Yang, Boosting face recognition in real-world surveillance videos, in AVSS (2012)Google Scholar
  3. 3.
    L. An, M. Kafai, B. Bhanu, Dynamic Bayesian network for unconstrained face recognition in surveillance camera networks. IEEE J. Emerging Sel. Top. Circuits Syst. 3(2), 155–164 (2013)CrossRefGoogle Scholar
  4. 4.
    S. Arashloo, J. Kittler, Energy normalization for pose-invariant face recognition based on MRF model image matching. IEEE Trans. Pattern Anal. Mach. Intell. 33(6), 1274–1280 (2011)CrossRefGoogle Scholar
  5. 5.
    A. Asthana, T. Marks, M. Jones, K. Tieu, M. Rohith, Fully automatic pose-invariant face recognition via 3D pose normalization, in ICCV (2011), pp. 937–944Google Scholar
  6. 6.
    M. Bae, A. Razdan, G. Farin, Automated 3D face authentication & recognition, in AVSS (2007), pp. 45–50Google Scholar
  7. 7.
    C. Castillo, D. Jacobs, Using stereo matching for 2-D face recognition across pose, in CVPR (2007), pp. 1–8Google Scholar
  8. 8.
    X. Chai, S. Shan, X. Chen, W. Gao, Locally linear regression for pose-invariant face recognition. IEEE Trans. Image Process. 16(7), 1716–1725 (2007)MathSciNetCrossRefGoogle Scholar
  9. 9.
    D. Chen, X. Cao, L. Wang, F. Wen, J. Sun, Bayesian face revisited: a joint formulation, in ECCV (2012), pp. 566–579Google Scholar
  10. 10.
    D. Chen, X. Cao, F. Wen, J. Sun, Blessing of dimensionality: high-dimensional feature and its efficient compression for face verification, in CVPR (2013), pp. 3025–3032Google Scholar
  11. 11.
    Z. Cui, S. Shan, H. Zhang, S. Lao, X. Chen, Image sets alignment for video-based face recognition, in CVPR (2012), pp. 2626–2633Google Scholar
  12. 12.
    N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in CVPR, vol. 1 (2005), pp. 886–893Google Scholar
  13. 13.
    R.P. Duin, E. Pekalska, The dissimilarity space: bridging structural and statistical pattern recognition. Pattern Recogn. Lett. 33(7), 826–832 (2012)CrossRefGoogle Scholar
  14. 14.
    Y. Gao, Y. Qi, Robust visual similarity retrieval in single model face databases, Pattern Recogn. 38, 1009–1020 (2005)CrossRefGoogle Scholar
  15. 15.
    R. Gross, I. Matthews, J. Cohn, T. Kanade, S. Baker, Multi-PIE. Image Vis. Comput. 28(5), 807–813 (2010)CrossRefGoogle Scholar
  16. 16.
    Y. Guo, Y. Shan, H. Sawhney, R. Kumar, Peet: prototype embedding and embedding transition for matching vehicles over disparate viewpoints, in CVPR (2007), pp. 1–8Google Scholar
  17. 17.
    A. Gyaourova, A. Ross, Index codes for multibiometric pattern retrieval. IEEE Trans. Inf. Forensics Secur. 7(2), 518–529 (2012)CrossRefGoogle Scholar
  18. 18.
    J. Huang, P. Yuen, W.S. Chen, J. Lai, Component-based LDA method for face recognition with one training sample, in IEEE International Workshop on Analysis and Modeling of Faces and Gestures (2003)Google Scholar
  19. 19.
    J. Huang, P. Yuen, W.S. Chen, J.H. Lai, Choosing parameters of kernel subspace LDA for recognition of face images under pose and illumination variations. IEEE Trans. Syst. Man Cybern. B Cybern. 37(4), 847–862 (2007)CrossRefGoogle Scholar
  20. 20.
    G.B. Huang, M. Ramesh, T. Berg, E. Learned-Miller, Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical Report 07–49, UMass Amherst (2007)Google Scholar
  21. 21.
    D. Huang, C. Shan, M. Ardabilian, Y. Wang, L. Chen, Local binary patterns and its application to facial image analysis: a survey, IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 41(6), 765–781 (2011)CrossRefGoogle Scholar
  22. 22.
    H.Y. Jie, H. Yu, J. Yang, A direct LDA algorithm for high-dimensional data – with application to face recognition. Pattern Recogn. 34, 2067–2070 (2001)CrossRefzbMATHGoogle Scholar
  23. 23.
    M. Kafai, B. Bhanu, L. An, Cluster-classification Bayesian networks for head pose estimation, in ICPR (2012)Google Scholar
  24. 24.
    M. Kafai, K. Eshghi, B. Bhanu, Discrete cosine transform locality-sensitive hashes for face retrieval. IEEE Trans. Multimedia 16(4), 1090–1103 (2014)CrossRefGoogle Scholar
  25. 25.
    F. Kahraman, B. Kurt, M. Gokmen, Robust face alignment for illumination and pose invariant face recognition, in CVPR (2007)Google Scholar
  26. 26.
    N. Kumar, A. Berg, P. Belhumeur, S. Nayar, Attribute and simile classifiers for face verification, in ICCV (2009), pp. 365–372Google Scholar
  27. 27.
    O. Langner, R. Dotsch, G. Bijlstra, D. Wigboldus, S. Hawk, A. Van Knippenberg, Presentation and validation of the Radboud faces database. Cogn. Emot. 24(8), 1377–388 (2010)CrossRefGoogle Scholar
  28. 28.
    A. Li, S. Shan, W. Gao, Coupled bias-variance tradeoff for cross-pose face recognition. IEEE Trans Image Process. 21(1), 305–315 (2012)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Q. Liao, J.Z. Leibo, T. Poggio, Learning invariant representations and applications to face verification, in Advances in Neural Information Processing Systems (NIPS), (2013), pp. 3057–3065Google Scholar
  30. 30.
    D. Little, S. Krishna, J. Black, S. Panchanathan, A methodology for evaluating robustness of face recognition algorithms with respect to variations in pose angle and illumination angle, in ICASSP (2005)Google Scholar
  31. 31.
    J. Liu, B. Kuipers, S. Savarese, Recognizing human actions by attributes, in CVPR (2011), pp. 3337–3344Google Scholar
  32. 32.
    A. Majumdar, R. Ward, Pseudo-Fisherface method for single image per person face recognition, in IEEE International Conference on Acoustics, Speech and Signal Processing (2008), pp. 989–992Google Scholar
  33. 33.
    Y. Mami, D. Charlet, Speaker recognition by location in the space of reference speakers. Speech Comm. 48(2), 127–141 (2006)CrossRefGoogle Scholar
  34. 34.
    H.V. Nguyen, L. Bai, Cosine similarity metric learning for face verification, in ACCV (2010), pp. 709–720Google Scholar
  35. 35.
    Y. Peng, A. Ganesh, J. Wright, W. Xu, Y. Ma, RASL: robust alignment by sparse and low-rank decomposition for linearly correlated images. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2233–2246 (2012)CrossRefGoogle Scholar
  36. 36.
    P. Phillips, H. Moon, P. Rauss, S. Rizvi, The FERET evaluation methodology for face-recognition algorithms, in CVPR (1997)Google Scholar
  37. 37.
    U. Prabhu, J. Heo, M. Savvides, Unconstrained pose-invariant face recognition using 3D generic elastic models. IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 1952–1961 (2011)CrossRefGoogle Scholar
  38. 38.
    S. Prince, J. Warrell, J. Elder, F. Felisberti, Tied factor analysis for face recognition across large pose differences. IEEE Trans. Pattern Anal. Mach. Intell. 30(6), 970–984 (2008)CrossRefGoogle Scholar
  39. 39.
    N. Rasiwasia, P. Moreno, N. Vasconcelos, Bridging the gap: query by semantic example. IEEE Trans. Multimedia 9(5), 923–938 (2007)CrossRefGoogle Scholar
  40. 40.
    K. Sakata, T. Maeda, M. Matsushita, K. Sasakawa, H. Tamaki, Fingerprint authentication based on matching scores with other data, in International Conference on Advances in Biometrics (2006), pp. 280–286Google Scholar
  41. 41.
    F. Schroff, T. Treibitz, D. Kriegman, S. Belongie, Pose, illumination and expression invariant pairwise face-similarity measure via Doppelgänger list comparison, in ICCV (2011), pp. 2494–2501Google Scholar
  42. 42.
    W. Shen, B. Wang, Y. Wang, X. Bai, L.J. Latecki, Face identification using reference-based features with message passing model. Neurocomputing 99, 339–346 (2013)CrossRefGoogle Scholar
  43. 43.
    T. Sim, S. Baker, M. Bsat, The CMU pose, illumination, and expression (PIE) database, in FG (2002), pp. 46–51Google Scholar
  44. 44.
    R. Singh, M. Vatsa, A. Ross, A. Noore, A mosaicing scheme for pose-invariant face recognition. IEEE Trans. Syst. Man Cybern. B Cybern. 37(5), 1212–1225 (2007)CrossRefGoogle Scholar
  45. 45.
    X. Tan, B. Triggs, Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)MathSciNetCrossRefGoogle Scholar
  46. 46.
    X. Tan, S. Chen, Z.H. Zhou, F. Zhang, Face recognition from a single image per person: a survey. Pattern Recogn. 39(9), 1725–1745 (2006)CrossRefzbMATHGoogle Scholar
  47. 47.
    C.E. Thomaz, G.A. Giraldi, A new ranking method for principal components analysis and its application to face image analysis. Image Vis. Comput. 28(6), 902–913 (2010)CrossRefGoogle Scholar
  48. 48.
    M. Turk, A. Pentland, Face recognition using eigenfaces, in CVPR (1991)Google Scholar
  49. 49.
    S. Wold, K. Esbensen, P. Geladi, Principal component analysis. Chemom. Intell. Lab. Syst. 2(1–3), 37–52 (1987)CrossRefGoogle Scholar
  50. 50.
    L. Wolf, T. Hassner, Y. Taigman, Effective unconstrained face recognition by combining multiple descriptors and learned background statistics. IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 1978–1990 (2011)CrossRefGoogle Scholar
  51. 51.
    X. Xianchuan, Z. Qi, Medical image retrieval using local binary patterns with image Euclidean distance, in International Conference on Information Engineering and Computer Science (2009), pp. 1–4Google Scholar
  52. 52.
    Q. Yin, X. Tang, J. Sun, An associate-predict model for face recognition, in CVPR (2011), pp. 497–504Google Scholar
  53. 53.
    X. Zhang, Y. Gao, Face recognition across pose: a review. Pattern Recogn. 42(11), 2876–2896 (2009)CrossRefGoogle Scholar
  54. 54.
    W. Zhang, S. Shan, W. Gao, X. Chen, H. Zhang, Local gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition, in ICCV (2005), pp. 786–791Google Scholar
  55. 55.
    X. Zhang, Y. Gao, M. Leung, Recognizing rotated faces from frontal and side views: an approach toward effective use of mugshot databases. IEEE Trans. Inf. Forensics Secur. 3(4) 684–697 (2008)CrossRefGoogle Scholar
  56. 56.
    Y. Zhang, M. Shao, E. Wong, Y. Fu, Random faces guided sparse many-to-one encoder for pose-invariant face recognition, in ICCV (2013), pp. 2416–2423Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Hewlett Packard LabsPalo AltoUSA
  2. 2.BRIC, University of North Carolina at Chapel HillChapel HillUSA
  3. 3.CRIS, University of CaliforniaRiversideUSA

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