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On robust face recognition via sparse coding: the good, the bad and the ugly

On robust face recognition via sparse coding: the good, the bad and the ugly

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In the field of face recognition, sparse representation (SR) has received considerable attention during the past few years, with a focus on holistic descriptors in closed-set identification applications. The underlying assumption in such SR-based methods is that each class in the gallery has sufficient samples and the query lies on the subspace spanned by the gallery of the same class. Unfortunately, such an assumption is easily violated in the face verification scenario, where the task is to determine if two faces (where one or both have not been seen before) belong to the same person. In this study, the authors propose an alternative approach to SR-based face verification, where SR encoding is performed on local image patches rather than the entire face. The obtained sparse signals are pooled via averaging to form multiple region descriptors, which then form an overall face descriptor. Owing to the deliberate loss of spatial relations within each region (caused by averaging), the resulting descriptor is robust to misalignment and various image deformations. Within the proposed framework, they evaluate several SR encoding techniques: l 1-minimisation, Sparse Autoencoder Neural Network (SANN) and an implicit probabilistic technique based on Gaussian mixture models. Thorough experiments on AR, FERET, exYaleB, BANCA and ChokePoint datasets show that the local SR approach obtains considerably better and more robust performance than several previous state-of-the-art holistic SR methods, on both the traditional closed-set identification task and the more applicable face verification task. The experiments also show that l 1-minimisation-based encoding has a considerably higher computational cost when compared with SANN-based and probabilistic encoding, but leads to higher recognition rates.

References

    1. 1)
      • 1. Cardinaux, F., Sanderson, C., Bengio, S.: ‘User authentication via adapted statistical models of face images’, IEEE Trans. Signal Process., 2006, 54, (1), pp. 361373 (doi: 10.1109/TSP.2005.861075).
    2. 2)
      • 2. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: ‘Eigenfaces vs. fisherfaces: recognition using class specific linear projection’, IEEE Trans. Pattern Anal. Mach. Intell., 1997, 19, (7), pp. 711720 (doi: 10.1109/34.598228).
    3. 3)
      • 3. Doddington, G.R., Przybocki, M.A., Martin, A.F., Reynolds, D.A.: ‘The NIST speaker recognition evaluation – overview, methodology, systems, results, perspective’, Speech Commun., 2000, 31, (2-3), pp. 225254 (doi: 10.1016/S0167-6393(99)00080-1).
    4. 4)
      • 4. Ali, T., Veldhuis, R., Spreeuwers, L.: ‘Forensic face recognition: a survey’. Technical report TR-CTIT-10-40, Centre for Telematics and Information Technology, University of Twente, December 2010.
    5. 5)
      • 5. Tu, P.H., Doretto, G., Krahnstoever, N.O., et al: ‘An intelligent video framework for homeland protection’. Proc. SPIE Defence and Security Symp. – Unattended Ground, Sea, and Air Sensor Technologies and Applications IX, 2007, vol. 6562.
    6. 6)
      • 6. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: ‘Robust face recognition via sparse representation’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (2), pp. 210227 (doi: 10.1109/TPAMI.2008.79).
    7. 7)
      • 7. Yang, J., Zhang, L., Xu, Y., Yang, J.-Y.: ‘Beyond sparsity: the role of l1-optimizer in pattern classification’, Pattern Recognit., 2012, 45, (3), pp. 11041118 (doi: 10.1016/j.patcog.2011.08.022).
    8. 8)
      • 8. Yang, M., Zhang, L.: ‘Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary’. ECCV (6), (LNCS, 6316), 2010, pp. 448461.
    9. 9)
      • 9. Yang, M., Zhang, L., Yang, J., Zhang, D.: ‘Robust sparse coding for face recognition’. IEEE Conf. Computer Vision and Pattern Recognition, 2011, pp. 625632.
    10. 10)
      • 10. Yang, M., Zhang, L., Feng, X., Zhang, D.: ‘Fisher discrimination dictionary learning for sparse representation’. IEEE Int. Conf. Computer Vision, 2011, pp. 543550.
    11. 11)
      • 11. Fisher, R.A.: ‘The use of multiple measurements in taxonomic problems’, Annals Eugen., 1936, 7, pp. 179188 (doi: 10.1111/j.1469-1809.1936.tb02137.x).
    12. 12)
      • 12. Shi, Q., Eriksson, A., van den Hengel, A., Shen, C.: ‘Is face recognition really a compressive sensing problem?IEEE Conf. Computer Vision and Pattern Recognition, 2011, pp. 553560.
    13. 13)
      • 13. Harandi, M.T., Ahmadabadi, M.N., Araabi, B.N.: ‘Optimal local basis: a reinforcement learning approach for face recognition’, Int. J. Comput. Vis., 2009, 81, (2), pp. 191204 (doi: 10.1007/s11263-008-0161-5).
    14. 14)
      • 14. Turk, M., Pentland, A.: ‘Eigenfaces for recognition’, J. Cognitive Neurosci., 1991, 3, (1), pp. 7186 (doi: 10.1162/jocn.1991.3.1.71).
    15. 15)
      • 15. Torralba, A., Shina, P.: ‘Detecting faces in improverished images’. Technical Report 028, MIT AI Lab, 2001.
    16. 16)
      • 16. Rodriguez, Y., Cardinaux, F., Bengio, S., Mariéthoz, J.: ‘Measuring the performance of face localization systems’, Image Vis. Comput., 2006, 24, (8), pp. 882893 (doi: 10.1016/j.imavis.2006.02.012).
    17. 17)
      • 17. Wagner, A., Wright, J., Ganesh, A., Zhou, Z., Mobahi, H., Ma, Y.: ‘Towards a practical face recognition system: robust alignment and illumination by sparse representation’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (2), pp. 372386 (doi: 10.1109/TPAMI.2011.112).
    18. 18)
      • 18. Heisele, B., Ho, P., Wu, J., Poggio, T.: ‘Face recognition: component-based versus global approaches’, Comput. Vis. Image Underst., 2003, 91, (1-2), pp. 621 (doi: 10.1016/S1077-3142(03)00073-0).
    19. 19)
      • 19. Sanderson, C., Bengio, S., Gao, Y.: ‘On transforming statistical models for non-frontal face verification’, Pattern Recognit., 2006, 39, (2), pp. 288302 (doi: 10.1016/j.patcog.2005.07.001).
    20. 20)
      • 20. Aharon, M., Elad, M., Bruckstein, A.: ‘K-SVD: an algorithm for designing over complete dictionaries for sparse representation’, IEEE Trans. Signal Process., 2006, 54, (11), pp. 43114322 (doi: 10.1109/TSP.2006.881199).
    21. 21)
      • 21. Ekenel, H.K., Stiefelhagen, R.: ‘Local appearance based face recognition using discrete cosine transform’. European Signal Processing Conf., 2005.
    22. 22)
      • 22. Csurka, G., Dance, C.R., Fan, L., Willamowski, J., Bray, C.: ‘Visual categorization with bags of key points’. In Workshop on Statistical Learning in Computer Vision, ECCV, 2004, pp. 122.
    23. 23)
      • 23. Lazebnik, S., Schmid, C., Ponce, J.: ‘Beyond bags of features: spatial pyramid matching for recognizing natural scene categories’. IEEE Conf. Computer Vision and Pattern Recognition, 2006, pp. 21692178.
    24. 24)
      • 24. Sanderson, C., Lovell, B.C.: ‘Multi-region probabilistic histograms for robust and scalable identity inference’. (LNCS, 5558), 2009, pp. 199208.
    25. 25)
      • 25. Coates, A., Lee, H., Ng, A.Y.: ‘An analysis of single-layer networks in unsupervised feature learning’, J. Mach. Learn. Res., Proc. Track, 2011, 15, pp. 215223.
    26. 26)
      • 26. Wong, Y., Harandi, M.T., Sanderson, C., Lovell, B.C.: ‘On robust biometric identity verification via sparse enbcoding of faces: holistic vs local approaches’. IEEE Int. Joint Conf. Neural Networks, 2012, pp. 17621769.
    27. 27)
      • 27. Tropp, J.A., Wright, S.J.: ‘Computational methods for sparse solution of linear inverse problems’, Proc. IEEE, 2010, 98, (6), pp. 948958 (doi: 10.1109/JPROC.2010.2044010).
    28. 28)
      • 28. Chen, S.S., Donoho, D.L., Saunders, M.A.: ‘Atomic decomposition by basis pursuit’, SIAM Rev., 2001, 43, (1), pp. 129159 (doi: 10.1137/S003614450037906X).
    29. 29)
      • 29. Tropp, J.A.: ‘Greed is good: algorithmic results for sparse approximation’, IEEE Trans. Inf. Theory, 2004, 50, (10), pp. 22312242 (doi: 10.1109/TIT.2004.834793).
    30. 30)
      • 30. Coates, A., Ng, A.Y.: ‘The importance of encoding versus training with sparse coding and vector quantization’. Proc. Int. Conf. Machine Learning, June 2011, pp. 921928.
    31. 31)
      • 31. Rubinstein, R., Bruckstein, A.M., Elad, M.: ‘Dictionaries for sparse representation modeling’, Proc. IEEE, 2010, 98, (6), pp. 10451057 (doi: 10.1109/JPROC.2010.2040551).
    32. 32)
      • 32. Tropp, J.A., Gilbert, A.C., Strauss, M.J.: ‘Algorithms for simultaneous sparse approximation. Part I: greedy pursuit’, Signal Process., 2006, 86, (3), pp. 572588 (doi: 10.1016/j.sigpro.2005.05.030).
    33. 33)
      • 33. Bishop, C.M.: ‘Neural networks for pattern recognition’ (Oxford University Press, 1995, 1st edn.).
    34. 34)
      • 34. Ranzato, M., Boureau, Y.-L., LeCun, Y.: ‘Sparse feature learning for deep belief networks’. NIPS, 2007.
    35. 35)
      • 35. Good fellow, I.J., Le, Q.V., Saxe, A.M., Lee, H., Ng, A.Y.: ‘Measuring invariances in deep networks’, Adv. Neural Inf. Process. Syst., 2009, pp. 646654.
    36. 36)
      • 36. Reynolds, D.A.: ‘Gaussian mixture models’. Encyclopedia of Biometrics, 2009, pp. 659663.
    37. 37)
      • 37. Bishop, C.M.: ‘Pattern recognition and machine learning’ (Springer, 2006).
    38. 38)
      • 38. Duda, R.O., Hart, P.E., Stork, D.G.: ‘Pattern classification’ (Wiley, 2001, 2nd edn.).
    39. 39)
      • 39. Gonzalez, R., Woods, R.: ‘Digital image processing’ (Prentice-Hall, 2007, 3rd edn.).
    40. 40)
      • 40. Cui, Z., Shan, S., Chen, X., Zhang, L.: ‘Sparsely encoded local descriptor for face recognition’. IEEE Int. Conf. Automatic Face & Gesture Recognition and Workshops, 2011, pp. 149154.
    41. 41)
      • 41. Lee, K.-C., Ho, J., Kriegman, D.J.: ‘Acquiring linear subspaces for face recognition under variable lighting’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (5), pp. 684698 (doi: 10.1109/TPAMI.2005.92).
    42. 42)
      • 42. Ahonen, T., Hadid, A., Pietikäinen, M.: ‘Face description with local binary patterns: application to face recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (12), pp. 20372041 (doi: 10.1109/TPAMI.2006.244).
    43. 43)
      • 43. Bruckstein, A.M., Elad, M., Zibulevsky, M.: ‘On the uniqueness of nonnegative sparse solutions to underdetermined systems of equations’, IEEE Trans. Inf. Theory, 2008, 54, (11), pp. 48134820 (doi: 10.1109/TIT.2008.929920).
    44. 44)
      • 44. Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: ‘The FERET database and evaluation procedure for face-recognition algorithms’, Image Vis. Comput., 1998, 16, (5), pp. 295306 (doi: 10.1016/S0262-8856(97)00070-X).
    45. 45)
      • 45. Martínez, A., Benavente, R.: ‘The AR face database’. CVC Technical Report 24, Computer Vision Center, Universitat Autónoma de Barcelona, June 1998.
    46. 46)
      • 46. Bailly-Bailliére, E., Bengio, S., Bimbot, F., et al: ‘The BANCA database and evaluation protocol’, Audio- and Video-based Biometric Person Authentication (AVBPA), (LNCS, 2688), 2003, pp. 625638 (doi: 10.1007/3-540-44887-X_74).
    47. 47)
      • 47. Wong, Y., Chen, S., Mau, S., Sanderson, C., Lovell, B.C.: ‘Patch-based probabilistic image quality assessment for face selection and improved video-based face recognition’. Computer Vision and Pattern Recognition Workshops (CVPRW), 2011, pp. 7481.
    48. 48)
      • 48. Gao, W., Cao, B., Shan, S., et al: ‘The CAS-PEAL large-scale Chinese face database and baseline evaluations’, IEEE Trans. Syst. Man Cybern. A, Syst. Humans, 2008, 38, (1), pp. 149161 (doi: 10.1109/TSMCA.2007.909557).
    49. 49)
      • 49. Bengio, S., Mariéthoz, J.: ‘The expected performance curve: a new assessment measure for person authentication’. Proc. Odyssey 2004: The Speaker and Language Recognition Workshop, 2004, pp. 279284.
    50. 50)
      • 50. Liu, C., Wechsler, H.: ‘Gabor feature based classification using the enhanced Fisher linear discriminant model for face recognition’, IEEE Trans. Image Process., 2002, 11, (4), pp. 467476 (doi: 10.1109/TIP.2002.999679).
    51. 51)
      • 51. Trefethen, L.N., Bau, D.: ‘Numerical linear algebra’ (Society for Industrial and Applied Mathematics, SIAM, 1997).
    52. 52)
      • 52. Huttenocher, D.P., Klanderman, G.A., Rucklidge, W.: ‘Comparing images using the Hausdorff distance’, IEEE Trans. Pattern Anal. Mach. Intell., 1993, 15, (9), pp. 850863 (doi: 10.1109/34.232073).
    53. 53)
      • 53. Chen, S., Mau, S., Harandi, M.T., Sanderson, C., Bigdeli, A., Lovell, B.C.: ‘Face recognition from still images to video sequences: a local-feature-based framework’, EURASIP J. Image Video Process., 2011, 2011.
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