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Measuring the degree of face familiarity based on extended NMF

Published:04 June 2013Publication History
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Abstract

Getting familiar with a face is an important cognitive process in human perception of faces, but little study has been reported on how to objectively measure the degree of familiarity. In this article, a method is proposed to quantitatively measure the familiarity of a face with respect to a set of reference faces that have been seen previously. The proposed method models the context-free and context-dependent forms of familiarity suggested by psychological studies and accounts for the key factors, namely exposure frequency, exposure intensity and similar exposure, that affect human perception of face familiarity. Specifically, the method divides the reference set into nonexclusive groups and measures the familiarity of a given face by aggregating the similarities of the face to the individual groups. In addition, the nonnegative matrix factorization (NMF) is extended in this paper to learn a compact and localized subspace representation for measuring the similarities of the face with respect to the individual groups. The proposed method has been evaluated through experiments that follow the protocols commonly used in psychological studies and has been compared with subjective evaluation. Results have shown that the proposed measurement is highly consistent with the subjective judgment of face familiarity. Moreover, a face recognition method is devised using the concept of face familiarity and the results on the standard FERET evaluation protocols have further verified the efficacy of the proposed familiarity measurement.

References

  1. Ahonen, T., Hadid, A., and Pietikäinen, M. 2006. Face description with local binary patterns: Application to face recognition. IEEE Trans. Patt. Anal. Mach. Intell. 28, 12, 2037--2041. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Bekios-Calfa, J., Buenaposada, J. M., and Baumela, L. 2011. Revisiting linear discriminant techniques in gender recognition. IEEE Trans. Patt. Anal. Mach. Intell. 33, 4, 858--864. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Belhumeur, P. N., Hespanha, J., and Kriegman, D. 1997. Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. Patt. Anal. Mach. Intell. 19, 7, 711--720. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Bertsekas, D. P. 1976. On the Goldstein-Levitin-Polyak gradient projection method. IEEE Trans. Automat. Cont. 21, 174--184.Google ScholarGoogle ScholarCross RefCross Ref
  5. Bertsekas, D. P. 1999. Nonlinear Programming. Athena Scientific.Google ScholarGoogle Scholar
  6. Bicego, M., Grosso, E., Lagorio, A., Brelstaff, G., Brodo, L., and Tistarelli, M. 2008. Distinctiveness of faces: A computational approach. ACM Trans. Appl. Percep. 5, 2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Bruyer, R., Mejiasa, S., and Doubleta, S. 2007. Effect of face familiarity on age decision. Acta Psychologica 124, 2, 159--176.Google ScholarGoogle ScholarCross RefCross Ref
  8. Cai, D., He, X., Han, J., and Huang, T. S. 2001. Graph regularized non-negative matrix factorization for data representation. IEEE Trans. Patt. Anal. Mach. Intell. 33, 8, 1548--1560. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Fasel, B. and Luttin, J. 2003. Automatic facial expression analysis: Survey. Patt. Recog. 36, 1, 259--275.Google ScholarGoogle ScholarCross RefCross Ref
  10. Frischholz, R. and Dieckmann, U. 2000. BioID: A multimodal biometric identification system. Computer. 33, 2, 64--68. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Fu, Y., Guo, G., and Huang, T. S. 2010. Age synthesis and estimation via faces: A survey. IEEE Trans. Patt. Anal. Mach. Intell. 32, 11, 1955--1976. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Geng, X., Zhou, Z.-H., and Smith-Miles, K. 2007. Automatic age estimation based on facial aging patterns. IEEE Trans. Patt. Anal. Mach. Intell. 29, 12, 2234--2240. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Gu, W., Xiang, G., Venkatesh, Y. V., Huang, D., and Lin, H. 2012. Facial expression recognition using radial encoding of local Gabor features and classifier synthesis. J. Patt. Recog. 45, 80--91. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Guan, N., Tao, D., Luo, Z., and Yuan, B. 2011. Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent. IEEE Trans. Image Proc. 20, 7, 2030--2048. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Hoyer, P. O. 2004. Non-negative matrix factorization with sparseness constraints. J. Mach. Learn. Res. 5, 5, 1457--1469. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. http://www.fantamorph.com/.Google ScholarGoogle Scholar
  17. Huynh-Thu, Q. and Ghanbari, M. 2008. Scope of validity of PSNR in image/video quality assessment. Electron. Lett. 44, 13, 800--801.Google ScholarGoogle ScholarCross RefCross Ref
  18. Kendall, M. and Gibbons, J. D. 1990. Rank Correlation Methods. Edward Arnold.Google ScholarGoogle Scholar
  19. Kinen, E. M. and Raisamo, R. 2008. Evaluation of gender classification methods with automatically detected and aligned faces. IEEE Trans. Patt. Anal. Mach. Intell. 30, 3, 541--547. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Lee, D. D. and Seung, H. S. 1999. Learning the parts of objects by non-negative matrix factorization. Nature. 401, 788--791.Google ScholarGoogle ScholarCross RefCross Ref
  21. Lee, D. D. and Seung, H. S. 2000. Algorithms for non-negative matrix factorization. In Proceedings of NIPS 2000. 556--562.Google ScholarGoogle Scholar
  22. Lee, D. D. and Seung, H. S. 2001. Algorithms for non-negative matrix factorization. Adv. Neural Inf. Proc. Syst. 13, 556--562.Google ScholarGoogle Scholar
  23. Li, S. Z., Hou, X., Zhang, H., and Cheng, Q. 2001. Learning spatially localized, parts-based representation. In Proceedings of CVPR 2001. Vol. 1. 207--212.Google ScholarGoogle Scholar
  24. Li, S. Z. and Jain, A. K. 2004. Handbook of Face Recognition. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Lin, C.-J. 2007a. On the convergence of multiplicative update algorithms for nonnegative matrix factorization. IEEE Trans. Neural. Netw. 18, 6, 1589--1596. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Lin, C.-J. 2007b. Projected gradient methods for nonnegative matrix factorization. Neur. Comput. 19, 10, 2756--2779. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Lin, C.-J. and More, J. J. 1999. Newton's method for large-scale bound constrained problems. SIAM J. Optimiz. 9, 1100--1127. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Liu, X., Yan, S., and Jin, H. 2010. Projective nonnegative graph embedding. IEEE Trans. Image Proc. 19, 5, 1126--1137. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Mandler, G. 1980. Recognizing: The judgement of previous occurrence. Psyc. Rev. 87, 252--271.Google ScholarGoogle ScholarCross RefCross Ref
  30. Martinez, A. and Benavente, R. 1998. The AR face database. Tech. rep. 24, CVC.Google ScholarGoogle Scholar
  31. Naseem, I., Togneri, R., and Bennamoun, M. 2010. Linear regression for face recognition. IEEE Trans. Patt. Anal. Mach. Intell. 32, 11, 2106--2112. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Paatero, P. and Tapper, U. 1994. Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values. Environmetrics 5, 2, 1180--4009.Google ScholarGoogle ScholarCross RefCross Ref
  33. Pantic, M. and Rothkrantz, L. 2000. Automatic analysis of facial expressions: the state of the art. IEEE Trans. Patt. Anal. Mach. Intell. 22, 12, 1424--1445. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Phillips, P., Wechsler, H., Huang, J., and Rauss, P. 1998. The FERET database and evaluation procedure for face recognition algorithms. Image Vis. Comp. 16, 295--306.Google ScholarGoogle ScholarCross RefCross Ref
  35. Rakover, S. S. and Cahlon, B. 2001. Face recognition: Cognitive and Computational Processes. (Illustrated Ed). John Benjamins Publishing Company.Google ScholarGoogle Scholar
  36. Rossion, B. 2002. Is sex categorisation from faces really parallel to face recognition? Vis. Cog. 9, 1003--1020.Google ScholarGoogle Scholar
  37. Samaria, F. and Harter, A. 1994. Parameterisation of a stochastic model for human face identification. In Proceedings of the 2nd IEEE Workshop on Applications of Computer Vision, 138--142.Google ScholarGoogle Scholar
  38. Schwaninger, A., Lobmaier, J. S., Wallraven, C., and Collishaw, S. M. 2009. Two routes to face perception: Evidence from psychophysics and computational modeling. Cog. Sci. 33, 8, 1413--1440.Google ScholarGoogle ScholarCross RefCross Ref
  39. Sinha, P., Balas, B., Ostrovsky, Y., and Russell, R. 2006. Face recognition by humans: 19 results all computer vision researchers should know about. Proc. IEEE 94, 11, 1948--1962.Google ScholarGoogle ScholarCross RefCross Ref
  40. Solso, R. and McCarthy, J. 1981. Prototype formation of faces: A case of pseudo-memory. British J. Psych. 72, 499--503.Google ScholarGoogle ScholarCross RefCross Ref
  41. Stein, S. and Fink, G. A. 2011. A new method for combined face detection and identification using interest point descriptors. In Proceedings of FG 2011.Google ScholarGoogle Scholar
  42. Tistarelli, M., Bicego, M., and Grosso, E. 2009. Dynamic face recognition: From human to machine vision. Image Vision Comput. 27, 3, 222--232. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Turk, M. and Pentland, A. 1991. Eigenfaces for recognition. J. Cognit. Neurosci. 3, 1, 71--76. Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. Valentine, T. 1991. A unified account of the effects of distinctiveness, inversion, and race in face recognition. Quart. J. Exper. Psych. 43, A, 161--204.Google ScholarGoogle ScholarCross RefCross Ref
  45. Viola, P. and Jones, M. 2004. Robust real-time object detection. Int. J. Comput. Vis. 57, 2, 137--154. Google ScholarGoogle ScholarDigital LibraryDigital Library
  46. Wallis, G., Siebeck, U. E., Swann, K., Blanz, V., and Bülthoff, H. H. 2008. The prototype effect revisited: Evidence for an abstract feature model of face recognition. J. Vis. 8, 3, Art. 20.Google ScholarGoogle ScholarCross RefCross Ref
  47. Wang, C., Song, Z., Yan, S., Zhang, L., and Zhang, H. 2009. Multiplicative nonnegative graph embedding. In Proceedings of CVPR 2009. Vol. 5. 389--396.Google ScholarGoogle Scholar
  48. Wiskott, L., Fellous, J., Kuiger, N., and von der Malsburg, C. 1997. Face recognition by elastic bunch graph matching. IEEE Trans. Patt. Anal. Mach. Intell. 19, 775--779. Google ScholarGoogle ScholarDigital LibraryDigital Library
  49. Yang, J., Yang, S., Fu, Y., Li, X., and Huang, T. 2008. Non-negative graph embedding. In Proceedings of CVPR 2008. 1--8.Google ScholarGoogle Scholar
  50. Yonelinas, A. P. 2002. The nature of recollection and familiarity: A review of 30 years of research. J. Memory Lang. 46, 441517.Google ScholarGoogle ScholarCross RefCross Ref
  51. Zafeiriou, S., Tefas, A., Buciu, I., and Pitas, I. 2006. Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification. IEEE Trans. Neural Netw. 17, 3, 683--695. Google ScholarGoogle ScholarDigital LibraryDigital Library
  52. Zhan, C., Li, W., Ogunbona, P., and Safaei, F. 2007. Real-time facial feature point extraction. Adv. Multimed. Inf. Proc. (PCM2007). Lecture Notes in Computer Science, vol. 4810, Springer 88--97. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Zhang, T., Fang, B., Tang, Y. Y., He, G., and Wen, J. 2008. Topology preserving non-negative matrix factorization for face recognition. IEEE Trans. Image Proc. 17, 574--584. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Zhao, W., Chellappa, R., Rosenfeld, A., and Phillips, P. 2003. Face recognition: A literature survey. ACM Comput. Surv. 35, 4, 399--458. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Zhi, R., Flierl, M., Ruan, Q., and Kleijn, W. B. 2011. Graph-preserving sparse nonnegative matrix factorization with application to facial expression recognition. IEEE Trans. Syst. Man. Cybern. Part B 41, 38--52. Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Transactions on Applied Perception
      ACM Transactions on Applied Perception  Volume 10, Issue 2
      May 2013
      98 pages
      ISSN:1544-3558
      EISSN:1544-3965
      DOI:10.1145/2465780
      Issue’s Table of Contents

      Copyright © 2013 ACM

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      Publication History

      • Published: 4 June 2013
      • Accepted: 1 November 2012
      • Revised: 1 August 2012
      • Received: 1 November 2011
      Published in tap Volume 10, Issue 2

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